{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-synonym-discovery-with-knowledge","title":"Automatic Synonym Discovery with Knowledge Bases","arxiv_id":"1706.08186","date":"2017-06-25","proceeding":null,"authors":["Meng Qu","Xiang Ren","Jiawei Han"],"abstract":"Recognizing entity synonyms from text has become a crucial task in many\nentity-leveraging applications. However, discovering entity synonyms from\ndomain-specific text corpora (e.g., news articles, scientific papers) is rather\nchallenging. Current systems take an entity name string as input to find out\nother names that are synonymous, ignoring the fact that often times a name\nstring can refer to multiple entities (e.g., \"apple\" could refer to both Apple\nInc and the fruit apple). Moreover, most existing methods require training data\nmanually created by domain experts to construct supervised-learning systems. In\nthis paper, we study the problem of automatic synonym discovery with knowledge\nbases, that is, identifying synonyms for knowledge base entities in a given\ndomain-specific corpus. The manually-curated synonyms for each entity stored in\na knowledge base not only form a set of name strings to disambiguate the\nmeaning for each other, but also can serve as \"distant\" supervision to help\ndetermine important features for the task. We propose a novel framework, called\nDPE, to integrate two kinds of mutually-complementing signals for synonym\ndiscovery, i.e., distributional features based on corpus-level statistics and\ntextual patterns based on local contexts. In particular, DPE jointly optimizes\nthe two kinds of signals in conjunction with distant supervision, so that they\ncan mutually enhance each other in the training stage. At the inference stage,\nboth signals will be utilized to discover synonyms for the given entities.\nExperimental results prove the effectiveness of the proposed framework.","url_abs":"http://arxiv.org/abs/1706.08186v1","url_pdf":"http://arxiv.org/pdf/1706.08186v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-synonym-discovery-with-knowledge","repo_url":"https://github.com/mnqu/DPE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.08186","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}