{"forum": "rJx2g-qaTm", "submission_url": "https://openreview.net/forum?id=rJx2g-qaTm", "submission_content": {"title": "Synonym Expansion for Large Shopping Taxonomies", "authors": ["Adrian Boteanu", "Adam Kiezun", "Shay Artzi"], "authorids": ["boteanua@amazon.com", "akkiezun@amazon.com", "artzi@amazon.com"], "keywords": ["Ontology", "Taxonomy", "Synonym", "Shopping", "Search"], "TL;DR": "We use machine learning to generate synonyms for large shopping taxonomies.", "abstract": "We present an approach for expanding taxonomies with synonyms, or aliases. We target large shopping taxonomies, with thousands of nodes. A comprehensive set of entity aliases is an important component of identifying entities in unstructured text such as product reviews or search queries. Our method consists of two stages: we generate synonym candidates from WordNet and shopping search queries, then use a binary classi\ufb01er to \ufb01lter candidates. We process taxonomies with thousands of synonyms in order to generate over 90,000 synonyms. We show that using the taxonomy to derive contextual features improves classi\ufb01cation performance over using features from the target node alone.We show that our approach has potential for transfer learning between di\ufb00erent taxonomy domains, which reduces the need to collect training data for new taxonomies.", "pdf": "/pdf/e48039522f203e4f5e7c4d80d24d9c50a8387b44.pdf", "archival status": "Archival", "subject areas": ["Machine Learning", "Natural Language Processing", "Applications: Other"], "paperhash": "boteanu|synonym_expansion_for_large_shopping_taxonomies", "_bibtex": "@inproceedings{\nboteanu2019synonym,\ntitle={Synonym Expansion for Large Shopping Taxonomies},\nauthor={Adrian Boteanu and Adam Kiezun and Shay Artzi},\nbooktitle={Automated Knowledge Base Construction (AKBC)},\nyear={2019},\nurl={https://openreview.net/forum?id=rJx2g-qaTm}\n}"}, "submission_cdate": 1542459636125, "submission_tcdate": 1542459636125, "submission_tmdate": 1580939656341, "submission_ddate": null, "review_id": ["HkefHtIA-4", "SyljBpJVM4", "rJxJU57rz4"], "review_url": ["https://openreview.net/forum?id=rJx2g-qaTm&noteId=HkefHtIA-4", "https://openreview.net/forum?id=rJx2g-qaTm&noteId=SyljBpJVM4", "https://openreview.net/forum?id=rJx2g-qaTm&noteId=rJxJU57rz4"], "review_cdate": [1546705210004, 1547070786527, 1547151942943], "review_tcdate": [1546705210004, 1547070786527, 1547151942943], "review_tmdate": [1550269646639, 1550269646412, 1550269632633], "review_readers": [["everyone"], ["everyone"], ["everyone"]], "review_writers": [["AKBC.ws/2019/Conference"], ["AKBC.ws/2019/Conference"], ["AKBC.ws/2019/Conference"]], "review_reply_count": [{"replyCount": 0}, {"replyCount": 0}, {"replyCount": 0}], "review_replyto": ["rJx2g-qaTm", "rJx2g-qaTm", "rJx2g-qaTm"], "review_content": [{"title": "Simple approach for a practical use case", "review": "The authors describe and evaluate two approaches to collecting alternative aliases (synonyms) for entities in a taxonomy: expansion from WordNet synsets and from search queries followed by a binary classification to refine the generated candidate sets. Mitigating vocabulary mismatch in search applications provides a good motivating use case for ontology/taxonomy construction and is an important research direction.\n\nQuestions:\n* How were the negative samples for training the classifier selected in 4.3?\n* What is the overlap between the synonym sets generated using WordNet and the search queries?\n* Can the WordNet-generated candidates improve performance for aligning synonyms collected from search queries? i.e. output of the first method as input to the second synonym selection method.\n* Are there other evaluation results that can show improvement from implementing the proposed approaches on the target tasks, e.g. search or information extraction?\n\nRemarks:\n* Semantic Network seems to be a synonym for a Knowledge Graph, which is a more frequently used term. The relation has to be made explicit.\n* The structure of the paper is confusing: only one of the candidate selection methods is described in the Section 3 but experimental results for two approaches are reported in Section 4.", "rating": "6: Marginally above acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, {"title": "Interesting approach to taxonomy extension", "review": "The paper presents an interesting approach to taxonomy extension that is based on identifying synonyms for component words of multi-word terms in the taxonomy. The approach seems to rely very much on WordNet, which may be a weakness. Coverage of WordNet is rather limited and the approach may therefore be limited in application. Also, word sense disambiguation (selecting the appropriate sense for a component word) is a challenge that has not been addressed in full detail, although this will be dealt with by the classification step in filtering, if I understand correctly. Overall, the paper is well-written and clear in ambitions and achieved results. The experiments use an extensive crowdsourced gold standard, which is a valuable research outcome on its own if it will be released publicly.", "rating": "7: Good paper, accept", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"}, {"title": "Authors propose a two stage synonym expansion framework for large shopping taxonomies. Paper is well written and empirical study is well conducted.", "review": "Authors present a method for expanding taxonomies with synonyms or aliases. The proposed method has two stages, 1) generate synonym candidates from WordNet and then 2) use a binary classifier to filter the candidates. The method is simple and effective. Paper is well written with ample empirical study and analysis. Couple of minor comments:\n1) Rather than using WordNet, for step 1, is it possible to use a similarity based clustering method to mine candidates for each concept from a corpus?\n2) For the word embeddings used in step 2, did the authors use off-the-shelf pre-compuated embeddings or compute the embeddings from a domain specific (in this case shopping) corpus? Will the performance improve if a domain specific embedding is applied?\n", "rating": "7: Good paper, accept", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"}], "comment_id": [], "comment_cdate": [], "comment_tcdate": [], "comment_tmdate": [], "comment_readers": [], "comment_writers": [], "comment_reply_content": [], "comment_content": [], "comment_replyto": [], "comment_url": [], "meta_review_cdate": 1549887387028, "meta_review_tcdate": 1549887387028, "meta_review_tmdate": 1551128222356, "meta_review_ddate ": null, "meta_review_title": "Interesting approach to taxonomy expansion", "meta_review_metareview": "The work provides an interesting and yet rather straightforward approach to synonym expansion that relies on a combination of user queries and existing background knowledge in terms of WordNet. The evaluation shows good results for an interesting domain in practice. It would be great if the crowd sourced data would be released. I also wonder if the paper actually covered enough of the related work in particular with respect to synonym expansion from information retrieval. \n\nOverall, I think the task itself and the resource would be interesting to have at the conference although it's not a radical innovation, hence, I would recommend it for the poster session. ", "meta_review_readers": ["everyone"], "meta_review_writers": [], "meta_review_reply_count": {"replyCount": 0}, "meta_review_url": ["https://openreview.net/forum?id=rJx2g-qaTm&noteId=SJx7ovJkBV"], "decision": "Accept (Poster)"}