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generation
semeval-2014
[ "Unlike HH up the block, this place actually gives you hearty and hot bagels this town is known for." ]
{'aspect_term': [['bagels', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I am not sure if I would call the food here Indian as it is a fusion of what seems to be French with an Indian or exotic touch." ]
{'aspect_term': [['food', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food is tasty and portion sizes are appropriate." ]
{'aspect_term': [['food', 'positive'], ['portion sizes', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Cheese plate is a varied delight and great bargain at $10." ]
{'aspect_term': [['Cheese plate', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Their wines by the glass are a great accompaniment and you can eat like a king with wine for under $30." ]
{'aspect_term': [['wines by the glass', 'positive'], ['wine', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The decor is nice, but more casual than fine dining." ]
{'aspect_term': [['decor', 'conflict']], 'aspect_category': [[None, 'conflict']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Not a large place, but it's cute and cozy." ]
{'aspect_term': [['place', 'conflict']], 'aspect_category': [[None, 'conflict']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "But the thing that my wife and I hated was it was so loud and it felt like 'bar' or 'pub'." ]
{'aspect_term': [['bar', 'negative'], ['pub', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I almost wanted to write a bad review, so no one would ever go here and I could have all the dumplings to myself!" ]
{'aspect_term': [['dumplings', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "If you are someone who appreciates the same things but hope to have food to spare or share, Kai may not be the best option." ]
{'aspect_term': [['food', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Furthermore, the rice had no seasoning, so the sushi was bland and disgusting." ]
{'aspect_term': [['rice', 'negative'], ['sushi', 'negative'], ['seasoning', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "A bit breezy up there on the mezzanine from the wind coming from the tracks." ]
{'aspect_term': [['mezzanine', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Normally that would be improper, however they were all delicious and my host did not complain." ]
{'aspect_term': [['host', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Most of the servers are very attentive, friendly and quite attractive." ]
{'aspect_term': [['servers', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I really like both the scallops and the mahi mahi (on saffron risotto-yum!)." ]
{'aspect_term': [['scallops', 'positive'], ['mahi mahi (on saffron risotto', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The menu seemed to have a wide variety of dishes for seafood lovers and interesting ways of preparing them." ]
{'aspect_term': [['menu', 'positive'], ['variety of dishes', 'positive'], ['seafood', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The staff was knowledgeable and full of personality." ]
{'aspect_term': [['staff', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Our waitress was sweet and accomodating, not overbearing." ]
{'aspect_term': [['waitress', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Similar to other Indian restaurants, they use the dinner special to attract customers at the door." ]
{'aspect_term': [['dinner special', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "If you love wine and cheese and delicious french fare, you'll love Artisanal!" ]
{'aspect_term': [['wine', 'positive'], ['french fare', 'positive'], ['cheese', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Maybe I say so because it looked promising for people who like artery-clogging jewish deli food, but turns out to be poorly run and awful." ]
{'aspect_term': [['jewish deli food', 'conflict']], 'aspect_category': [[None, 'conflict']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "But the best part about LS is the late night atmosphere, delightfully free of the BTs." ]
{'aspect_term': [['atmosphere', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Prices are in line." ]
{'aspect_term': [['Prices', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The staff was too busy ordering sushi for dinner and then laying it out to eat on the bar to even bring me my check." ]
{'aspect_term': [['staff', 'negative'], ['sushi', 'neutral'], ['check', 'neutral'], ['dinner', 'neutral'], ['bar', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral'], [None, 'neutral'], [None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Plus, on Wednesday nights the house wine is unlimited!" ]
{'aspect_term': [['house wine', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This is one great place to eat pizza more out but not a good place for take-out pizza." ]
{'aspect_term': [['pizza', 'positive'], ['take-out pizza', 'negative']], 'aspect_category': [[None, 'positive'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "No refills on fountain drinks, though." ]
{'aspect_term': [['fountain drinks', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Do not get the Go Go Hamburgers, no matter what the reviews say." ]
{'aspect_term': [['Go Go Hamburgers', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The burger was great, also." ]
{'aspect_term': [['burger', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "brick oven gallery is My pick for best pizza restaurant anywhere." ]
{'aspect_term': [['pizza', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The best thing I tasted were the lambchops." ]
{'aspect_term': [['lambchops', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "From the moment you enter till the moment you walk out the friendly and helpful staff was was just Fantastic." ]
{'aspect_term': [['staff', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The space is a bit too small for live music, so on jazz nights, it can be loud and cramped." ]
{'aspect_term': [['live music', 'neutral'], ['space', 'negative'], ['jazz nights', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "By far this is the only chinese desserts place I know in NY or anywhere close in the Northeastern America that serves desserts with frog jelly in a couple of varieties and pig feet ginger simmered in black vinegar." ]
{'aspect_term': [['pig feet ginger simmered in black vinegar', 'positive'], ['desserts with frog jelly', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation." ]
{'aspect_term': [['Traditional French decour', 'positive'], ['hall', 'negative']], 'aspect_category': [[None, 'positive'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Great food, good size menu, great service and an unpretensious setting." ]
{'aspect_term': [['food', 'positive'], ['menu', 'positive'], ['service', 'positive'], ['setting', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Tell them Herky sent you and get a free confused look from the waiter." ]
{'aspect_term': [['waiter', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I recommend the jelly fish, drunken chicken and the soupy dumplings, certainly the stir fry blue crab." ]
{'aspect_term': [['jelly fish', 'positive'], ['drunken chicken', 'positive'], ['soupy dumplings', 'positive'], ['stir fry blue crab', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The tables are crammed way too close, the menu is typical of any Italian restaurant, and the wine list is simply overpriced." ]
{'aspect_term': [['tables', 'negative'], ['menu', 'neutral'], ['wine list', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'neutral'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Okay service." ]
{'aspect_term': [['service', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We didn't even see a menu, as our waiter described both the specials and the main dishes." ]
{'aspect_term': [['menu', 'neutral'], ['main dishes', 'neutral'], ['waiter', 'positive'], ['specials', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral'], [None, 'positive'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This is the perfect date spot for Williamsburg couples." ]
{'aspect_term': [['date spot', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We started with lox and mussels (the best ive ever had, ever) and had the cod and trout for dinner." ]
{'aspect_term': [['lox', 'positive'], ['mussels', 'positive'], ['cod', 'neutral'], ['trout', 'neutral'], ['dinner', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'neutral'], [None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "But the thai is definitely not great -- bland and indistinguished." ]
{'aspect_term': [['thai', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Cute place, nice wait staff but would never go there again." ]
{'aspect_term': [['wait staff', 'positive'], ['place', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "What generous portions!" ]
{'aspect_term': [['portions', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food was delicious, the atmosphere was relaxed, and we have now adopted Plate 347 as our Secret on Second!" ]
{'aspect_term': [['food', 'positive'], ['atmosphere', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I had a terrific meal, and our server guided us toward a very nice wine in our price range, instead of allowing us to purchase a similarly priced wine that wasn't as good." ]
{'aspect_term': [['meal', 'positive'], ['server', 'positive'], ['wine', 'positive'], ['wine', 'negative'], ['price range', 'positive'], ['priced', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'negative'], [None, 'positive'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The staff is very kind and well trained, they're fast, they are always prompt to jump behind the bar and fix drinks, they know details of every item in the menu and make excelent recomendations." ]
{'aspect_term': [['staff', 'positive'], ['bar', 'neutral'], ['drinks', 'neutral'], ['menu', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'neutral'], [None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The ambience is very romantic and definitely a good place to bring a date." ]
{'aspect_term': [['ambience', 'positive'], ['place', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "we decided to eat in tea room which was small and cute." ]
{'aspect_term': [['tea room', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The wine the service was very good too." ]
{'aspect_term': [['wine', 'positive'], ['service', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I had to ask her three times before she finally came back with the dish Ive requested." ]
{'aspect_term': [['dish', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Service could be improved but overall this is a place that understands the importance of little things (the heavy, black, antique-seeming teapot, for one) in the restaurant experience." ]
{'aspect_term': [['Service', 'negative'], ['teapot', 'positive']], 'aspect_category': [[None, 'negative'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Having discovered Ping's on the internet, we entered with qualms but were instantly put to ease by the fish tanks that greet you as u walk in." ]
{'aspect_term': [['fish tanks', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I had the black cod with yuzu sauce, which was wonderful." ]
{'aspect_term': [['black cod with yuzu sauce', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I usually go there later at night when I get off work so I don't have to deal with crowds or lines." ]
{'aspect_term': [['lines', 'negative'], ['crowds', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The restaurant is rather small but we were lucky to get a table quickly." ]
{'aspect_term': [['table', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We had champagne and caviar and felt like princesses!" ]
{'aspect_term': [['champagne', 'positive'], ['caviar', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We went here for lunch a couple of weeks ago on a Saturday, and I was thoroughly impressed with the food." ]
{'aspect_term': [['lunch', 'neutral'], ['food', 'positive']], 'aspect_category': [[None, 'neutral'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Just don't take the seat between the bar and the back half of the restaurant, i saw a woman get nudged 40times sitting there." ]
{'aspect_term': [['seat', 'negative'], ['bar', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Oh, don't even let me start with how expensive the bills were!" ]
{'aspect_term': [['bills', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food was lousy - too sweet or too salty and the portions tiny." ]
{'aspect_term': [['food', 'negative'], ['portions', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Scalina Fedeli reminded me why service is so integral to fine dining." ]
{'aspect_term': [['service', 'positive'], ['dining', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The Pastrami sandwich was like buttah and with pickles and an icy cold beer to wash it down, it was a pleasurable experience." ]
{'aspect_term': [['Pastrami sandwich', 'positive'], ['beer', 'positive'], ['pickles', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Food was good not great not worth the wait or another visit" ]
{'aspect_term': [['Food', 'conflict'], ['wait', 'negative']], 'aspect_category': [[None, 'conflict'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "If you like the food and the value you get from some of Chinatown restaurants, this is not the place for you." ]
{'aspect_term': [['food', 'neutral'], ['value', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "sometimes i get good food and ok service." ]
{'aspect_term': [['food', 'positive'], ['service', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Were meeting up with some friends for a drink at Lafayette 161 and happened to walk by Thai Angel famished." ]
{'aspect_term': [['drink', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I like the ambience, it's very dark and original." ]
{'aspect_term': [['ambience', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The dosas are skimpy, unattractive and drip with grease, and personally I'd drink popcorn topping before I'd eat another one of these." ]
{'aspect_term': [['dosas', 'negative'], ['popcorn topping', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Waitstaff are very friendly." ]
{'aspect_term': [['Waitstaff', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "There is no excuse for such lousy service!" ]
{'aspect_term': [['service', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Salads were fantastic." ]
{'aspect_term': [['Salads', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Try the spicy wontons and the salt pepper shrimps." ]
{'aspect_term': [['spicy wontons', 'positive'], ['salt pepper shrimps', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This is the perfect spot for meeting friends, having lunch, dinner, pre-theatre or after-theatre drinks!" ]
{'aspect_term': [['lunch', 'positive'], ['dinner', 'positive'], ['pre-theatre or after-theatre drinks', 'positive'], ['spot', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The lunch special is an asbolute steal." ]
{'aspect_term': [['lunch special', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I'd highly recommend it for a special occasion -- it provides and intimate setting and nice service." ]
{'aspect_term': [['setting', 'positive'], ['service', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners, which is not good considering the bill was nearly $100." ]
{'aspect_term': [['mayonaisse', 'negative'], ['food runners', 'neutral'], ['bill', 'negative'], ['fries', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'neutral'], [None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Has the chef and owner changed???" ]
{'aspect_term': [['chef', 'neutral'], ['owner', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food was mediocre at best but it was the horrible service that made me vow never to go back." ]
{'aspect_term': [['food', 'neutral'], ['service', 'negative']], 'aspect_category': [[None, 'neutral'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Perhaps this food is considered extreme to an Upper East Side resident, but for the rest of us who've actually eaten ethnic food, this is simply dull." ]
{'aspect_term': [['food', 'conflict'], ['ethnic food', 'negative']], 'aspect_category': [[None, 'conflict'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The combination of fresh tomato sauce, fresh mozz cheese, basil and the dough they make with imported flour, makes this is one of the better pizza's in NY." ]
{'aspect_term': [['fresh tomato sauce', 'positive'], ['fresh mozz cheese', 'positive'], ['basil', 'positive'], ['dough', 'positive'], ['pizza', 'positive'], ['flour', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "You will pay a lot for the decore, but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY." ]
{'aspect_term': [['decore', 'negative'], ['food', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "And really large portions." ]
{'aspect_term': [['portions', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I cannot imagine better Indian food in all of the city." ]
{'aspect_term': [['Indian food', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Lucky Strike is a great casual place to just grab a bite to eat." ]
{'aspect_term': [['place', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "If you'd like to have a nice light meal with an asian accent, Long Tan is a good place on the slope." ]
{'aspect_term': [['meal', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Service was warm and attentive, beef carpaachio was exellent (huge portion) and pasta was fresh and well-prepared." ]
{'aspect_term': [['Service', 'positive'], ['beef carpaachio', 'positive'], ['pasta', 'positive'], ['portion', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Only complaint would be that at an average cost of $12-$15 per meal, I'd like not to have to worry about finding a seat!" ]
{'aspect_term': [['cost', 'negative'], ['meal', 'neutral'], ['seat', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'neutral'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The man that was hosting promised to save a table for our party of 7, then sat a party of 2 at the very table he was saving (mean while there were boths open all around)." ]
{'aspect_term': [['man', 'negative'], ['table', 'neutral'], ['table', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'neutral'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This is a great Thai restaurant with a very friendly staff." ]
{'aspect_term': [['staff', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I was here a few weeks back and we had the worst customer service experience at a restaurant ever." ]
{'aspect_term': [['customer service', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The table service could have been a little more attentive but as someone who also works in the service industry, I understood they were busy." ]
{'aspect_term': [['table service', 'conflict'], ['service', 'neutral']], 'aspect_category': [[None, 'conflict'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Be careful of portions - they're HUGE." ]
{'aspect_term': [['portions', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "People are always friendly." ]
{'aspect_term': [['People', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Not enough wines by the glass either." ]
{'aspect_term': [['wines by the glass', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I highly recommend visiting this restaurant and having dinner and drinks!" ]
{'aspect_term': [['dinner', 'positive'], ['drinks', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "In fact, while leaving the place we saw two people looking at the menu, and I couldn't help telling them that the food was horrible." ]
{'aspect_term': [['food', 'negative'], ['menu', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This was my frist time at Cafe St. Bart's and I must say how delicous the food and the service was." ]
{'aspect_term': [['food', 'positive'], ['service', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}