Datasets:

NEUDM

/

absa-quad

like 0

generation

absa-quad

["Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections ."]

[['vegetarian dishes', 'food quality', 'negative', 'not up to par']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['And it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3-6 each ) .']

[['NULL', 'food prices', 'positive', 'reasonable'], ['congee', 'food prices', 'positive', 'no more than $ 3-6 each'], ['noodles', 'food prices', 'positive', 'no more than $ 3-6 each'], ['rice dishes', 'food prices', 'positive', 'no more than $ 3-6 each']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['The cold appetizer dishes taste like the way I remember them to taste when I was growing up in Taiwan .']

[['cold appetizer dishes', 'food quality', 'positive', 'like the way I remember them']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['The appetizers are also delicious !']

[['appetizers', 'food quality', 'positive', 'delicious']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['The wine list is extensive and can easily hike up an otherwise reasonably priced meal .']

[['wine list', 'drinks style_options', 'positive', 'extensive'], ['meal', 'food prices', 'positive', 'reasonably priced']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Please take my advice , go and try this place .']

[['place', 'restaurant general', 'positive', 'go and try']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Decor needs to be upgraded but the food is amazing !']

[['Decor', 'ambience general', 'negative', 'upgraded'], ['food', 'food quality', 'positive', 'amazing']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Single Worst Restaurant in Manhattan']

[['Restaurant', 'restaurant general', 'negative', 'Worst']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Place is open till late , no dress code .']

[['Place', 'restaurant miscellaneous', 'positive', 'open till late']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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["I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) ."]

[['Edamame pureed', 'food quality', 'positive', 'tasty'], ['Edamame pureed', 'food style_options', 'positive', 'innovative']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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["Open late ( well as late as I ever got there and I 'm a night person )"]

[['NULL', 'restaurant miscellaneous', 'positive', 'well']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['The only thing I moderately enjoyed was their Grilled Chicken special with Edamame Puree .']

[['Grilled Chicken special with Edamame Puree', 'food quality', 'positive', 'enjoyed']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["I do n't appreciate places or people that try to drive up the bill without the patron 's knowledge so that was a huge turnoff ( more than the price ) ."]

[['NULL', 'service general', 'negative', 'drive up the bill'], ['NULL', 'restaurant prices', 'negative', 'huge turnoff']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Love their drink menu .']

[['drink menu', 'drinks style_options', 'positive', 'Love']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Bukhara is on my top 5 Indian places in NYC']

[['Bukhara', 'restaurant general', 'positive', 'top']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .']

[['location', 'location general', 'positive', 'good'], ['Suan', 'restaurant prices', 'positive', 'reasonable']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['This was , from start to finish , a mind-bogglingly uncomfortable experience .']

[['NULL', 'restaurant general', 'negative', 'mind-bogglingly uncomfortable']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Terrible , terrible management - deserves to be shut-down .']

[['management', 'service general', 'negative', 'Terrible'], ['management', 'service general', 'negative', 'terrible'], ['NULL', 'restaurant general', 'negative', 'deserves to be shut-down']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I took one look at the chicken and I was appalled .']

[['chicken', 'food style_options', 'negative', 'appalled']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .']

[['Appetizers', 'food quality', 'neutral', 'ok'], ['waiter', 'service general', 'negative', 'poor'], ['potato stuff kanish', 'food quality', 'positive', 'try'], ['potato stuff kanish', 'food quality', 'positive', 'best']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The bagel was huge .']

[['bagel', 'food style_options', 'positive', 'huge']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['My husband and I have been sold on this from the first visit .']

[['NULL', 'restaurant general', 'positive', 'sold on']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Excellent food , although the interior could use some help .']

[['food', 'food quality', 'positive', 'Excellent'], ['interior', 'ambience general', 'negative', 'help']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I look forward to eating here again']

[['NULL', 'restaurant general', 'positive', 'look forward']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .']

[['Areo', 'restaurant general', 'positive', 'enjoyable']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Service was very prompt but slightly rushed .']

[['Service', 'service general', 'positive', 'prompt'], ['Service', 'service general', 'positive', 'rushed']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['bottles of wine are cheap and good .']

[['bottles of wine', 'drinks prices', 'positive', 'cheap'], ['bottles of wine', 'drinks quality', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The menu has so many fish items and oysters .']

[['menu', 'food style_options', 'positive', 'so many']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["They 're rude at times , and not very friendly ."]

[['NULL', 'service general', 'negative', 'rude']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food was absolutely amazing ! !']

[['food', 'food quality', 'positive', 'amazing']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Terrible would be a compliment !']

[['NULL', 'restaurant general', 'negative', 'Terrible']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The menu looked great , and the waiter was very nice , but when the food came , it was average .']

[['menu', 'food style_options', 'positive', 'great'], ['waiter', 'service general', 'positive', 'nice'], ['food', 'food quality', 'neutral', 'average']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['We were greeted promptly by the waiter who was very nice and cordial .']

[['waiter', 'service general', 'positive', 'nice'], ['waiter', 'service general', 'positive', 'cordial']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Appetizers took nearly an hour .']

[['NULL', 'service general', 'negative', 'nearly an hour']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Great find in the West Village !']

[['NULL', 'restaurant general', 'positive', 'Great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["I wo n't go back unless someone else is footing the bill ."]

[['NULL', 'restaurant prices', 'negative', "wo n't go back"]]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Wine list selection is good and wine-by-the-glass was generously filled to the top .']

[['Wine list selection', 'drinks style_options', 'positive', 'good'], ['wine-by-the-glass', 'drinks style_options', 'positive', 'generously filled']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The people that work there are always so friendly you forget you are in New York sometimes .']

[['people', 'service general', 'positive', 'friendly']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Decent wine at reasonable prices .']

[['wine', 'drinks quality', 'positive', 'Decent'], ['wine', 'drinks prices', 'positive', 'reasonable']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I love it .']

[['NULL', 'restaurant general', 'positive', 'love']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I got the $ 10 10-piece dim sum combo , every bite of which was great .']

[['$ 10 10-piece dim sum combo', 'food quality', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["DO not try unless you 're just going there to hang out like the rest of the hipsters who apparently have no sense of taste ."]

[['NULL', 'restaurant miscellaneous', 'negative', 'Do not try']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Great food !']

[['food', 'food quality', 'positive', 'Great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Their pad penang is delicious and everything else is fantastic .']

[['pad penang', 'food quality', 'positive', 'delicious'], ['NULL', 'food quality', 'positive', 'fantastic']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I can not imagine a friendlier staff working in a restaurant .']

[['staff', 'service general', 'positive', 'friendlier']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Luckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .']

[['BBQ Salmon', 'food quality', 'positive', 'Luckily'], ['Sea Bass', 'food quality', 'positive', 'Luckily'], ['Crispy Duck', 'food quality', 'positive', 'Luckily']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Dokebi gives Williamsburg the right one-two punch of classic Korean food and fusion twists like pork belly tacos .']

[['Korean food', 'food quality', 'positive', 'classic'], ['fusion twists', 'food quality', 'positive', 'classic'], ['pork belly tacos', 'food quality', 'positive', 'classic']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["If you venture off the island of Manhattan and ca n't seem to find a great Italian restaurant , drive to Corona ."]

[['Corona', 'restaurant general', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['We all felt it was worth it .']

[['NULL', 'restaurant general', 'positive', 'worth']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Lucky Strike is a great casual place to just grab a bite to eat .']

[['Lucky Strike', 'restaurant general', 'positive', 'great casual']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I have been to Casimir over 5 times and I have always had a great time there .']

[['Casimir', 'restaurant general', 'positive', 'great time']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['great for a romantic evening , or a fun evening with friends ...']

[['NULL', 'ambience general', 'positive', 'romantic'], ['NULL', 'restaurant miscellaneous', 'positive', 'fun']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['But the best part about LS is the late night atmosphere , delightfully free of the BTs .']

[['late night atmosphere', 'ambience general', 'positive', 'best']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The dining room is quietly elegant with no music to shout over -- how refreshing !']

[['dining room', 'ambience general', 'positive', 'elegant'], ['dining room', 'ambience general', 'positive', 'refreshing']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['This is such a lovely , peaceful place to eat outside .']

[['place', 'ambience general', 'positive', 'lovely'], ['place', 'ambience general', 'positive', 'peaceful']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Do n't be fooled by crowds of people ."]

[['NULL', 'restaurant miscellaneous', 'negative', 'fooled']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['As to my comment about the food , no apology or acknowledgment was made .']

[['NULL', 'service general', 'negative', 'no apology or acknowledgment']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Service is top notch .']

[['Service', 'service general', 'positive', 'top notch']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['oh speaking of bathroom , the mens bathroom was disgusting .']

[['mens bathroom', 'ambience general', 'negative', 'disgusting']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["I 'll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty ."]

[['decor', 'ambience general', 'positive', 'cool'], ['pita', 'food quality', 'positive', 'good'], ['hummus', 'food quality', 'positive', 'good'], ['grilled octopus', 'food quality', 'positive', 'tasty']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .']

[['NULL', 'restaurant general', 'positive', 'DO NOT pass it up']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .']

[['raddichio', 'food quality', 'negative', 'bitter'], ['NULL', 'food quality', 'positive', 'delicious']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['If I could give 0 stars I would do so for this place .']

[['place', 'restaurant general', 'negative', '0 stars']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Amazing !']

[['NULL', 'food quality', 'positive', 'Amazing']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The hostess is rude to the point of being offensive .']

[['hostess', 'service general', 'negative', 'rude'], ['hostess', 'service general', 'negative', 'offensive']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The frizzy retro girl ( with winged/ Dame Edna glasses ) will yell at you if you try to order a drink .']

[['girl', 'service general', 'negative', 'frizzy']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['peppers , onions , relish , chilli , cheeses , you NAME it .']

[['NULL', 'food style_options', 'positive', 'you NAME it']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Great atmoshere and worth every bit .']

[['atmoshere', 'ambience general', 'positive', 'Great'], ['NULL', 'restaurant general', 'positive', 'worth']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Bison was quite excellent however .']

[['Bison', 'food quality', 'positive', 'excellent']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .']

[['food', 'food quality', 'positive', 'diamond'], ['balance of herbs and tomatoes', 'food quality', 'positive', 'perfect']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['It hits the spot every time']

[['NULL', 'restaurant general', 'positive', 'hits the spot']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .']

[['Food', 'food quality', 'positive', 'good'], ['view of the new york city skiline', 'location general', 'positive', 'terrific']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The lava cake dessert was incredible and I recommend it .']

[['lava cake dessert', 'food quality', 'positive', 'incredible'], ['lava cake dessert', 'food quality', 'positive', 'recommend']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['service is friendly , and never had a problem walking in and getting a table .']

[['service', 'service general', 'positive', 'friendly']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .']

[['Pizza', 'food quality', 'positive', 'crave']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I was here a few weeks back and we had the worst customer service experience at a restaurant ever .']

[['customer service', 'service general', 'negative', 'worst']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The spicy tuna roll was unusually good and the rock shrimp tempura was awesome , great appetizer to share !']

[['spicy tuna roll', 'food quality', 'positive', 'good'], ['rock shrimp tempura', 'food quality', 'positive', 'awesome']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The location and ambience is Ok but the food is what makes up for it .']

[['location', 'location general', 'neutral', 'Ok'], ['ambience', 'ambience general', 'neutral', 'Ok'], ['food', 'food quality', 'positive', 'makes up for it']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I noticed alot of indian people eatting there which is a great sign for an indian place !']

[['indian place', 'restaurant miscellaneous', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Always a nice crowd , but never loud .']

[['crowd', 'restaurant miscellaneous', 'positive', 'nice'], ['NULL', 'ambience general', 'positive', 'never loud']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['we love th pink pony .']

[['pink pony', 'restaurant general', 'positive', 'love']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Nevertheless , I finished my plate , and that 's when I found a maggot in mushroom sauce at the bottom ."]

[['mushroom sauce', 'food quality', 'negative', 'maggot']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I started out with a Bombay beer which was big enough for two .']

[['Bombay beer', 'drinks style_options', 'positive', 'big']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The view is spectacular , and the food is great .']

[['view', 'location general', 'positive', 'spectacular'], ['food', 'food quality', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['What really makes it shine is the food , which is aggressively seasoned with Cyrpriot spices , and all made in-house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .']

[['food', 'food quality', 'positive', 'shine'], ['gyro meat', 'food quality', 'positive', 'in-house'], ['sausages', 'food quality', 'positive', 'in-house'], ['ingredients', 'food quality', 'positive', 'higher quality']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['What a great place .']

[['place', 'restaurant general', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Which of course is not real Kobe but Wagyu beef .']

[['NULL', 'food quality', 'negative', 'not real']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Also , specify if you like your food spicy- its rather bland if you do n't ."]

[['food', 'food quality', 'negative', 'bland']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['They were served warm and had a soft fluffy interior .']

[['NULL', 'food quality', 'positive', 'warm'], ['NULL', 'food quality', 'positive', 'a soft fluffy interior']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !']

[['svc', 'service general', 'negative', 'rude'], ['restaurant', 'restaurant general', 'positive', 'must']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I had a great experience .']

[['NULL', 'restaurant general', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['MMMMMMMMMmmmmmm so delicious']

[['NULL', 'food quality', 'positive', 'delicious']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['It is thick and slightly soggy .']

[['NULL', 'food quality', 'negative', 'thick'], ['NULL', 'food quality', 'negative', 'soggy']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The first time the sushi was outstanding , the second time it was a little bland .']

[['sushi', 'food quality', 'positive', 'outstanding'], ['sushi', 'food quality', 'negative', 'bland']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Authentic Shanghai style and I can not recommend a better Shanghai place in New York .']

[['NULL', 'restaurant miscellaneous', 'positive', 'can not recommend a better']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food is flavorful , plentiful and reasonably priced .']

[['food', 'food quality', 'positive', 'flavorful'], ['food', 'food style_options', 'positive', 'plentiful'], ['food', 'food prices', 'positive', 'reasonably priced']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Have frequented 'ino for several years and the food remains excellent ."]

[['food', 'food quality', 'positive', 'excellent']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Gorgeous place ideal for a romantic dinner']

[['place', 'ambience general', 'positive', 'Gorgeous'], ['place', 'restaurant miscellaneous', 'positive', 'ideal']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['quacamole at pacifico is yummy , as are the wings with chimmichuri .']

[['quacamole', 'food quality', 'positive', 'yummy'], ['wings with chimmichuri', 'food quality', 'positive', 'yummy']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Cute and decorative .']

[['NULL', 'ambience general', 'positive', 'Cute'], ['NULL', 'ambience general', 'positive', 'decorative']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'