{"type":"ScholarlyArticle","title":"Entity Synonym Discovery via Multipiece Bilateral Context Matching","authors":[{"name":"Chenwei Zhang","slug":"chenwei-zhang","given":"Chenwei","family":"Zhang","affiliation":"Amazon , Seattle , WA 98109 USA"},{"name":"Yaliang Li","slug":"yaliang-li","given":"Yaliang","family":"Li","affiliation":"Alibaba Group , Bellevue , WA 98004 USA; University of Illinois at Chicago"},{"name":"Nan Du","slug":"nan-du","given":"Nan","family":"Du","affiliation":"Tencent Medical AI Lab , Palo Alto , CA 94306 USA"},{"name":"Wei Fan","slug":"wei-fan","given":"Wei","family":"Fan","affiliation":"Tencent Medical AI Lab , Palo Alto , CA 94306 USA"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"University of Illinois at Chicago , Chicago , IL 60607 USA"}],"abstract":"Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SynonymNet to determine whether or not two given entities are synonym with each other. Instead of using entities features, SynonymNet makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve and 3.19% in terms of Mean Average Precision compared to the best baseline method.","doi":"10.24963/ijcai.2020/199","url":"https://ijcai.org/proceedings/2020/199","pdf":"https://ijcai.org/proceedings/2020/0199.pdf","markdown":"https://ijcai.org/proceedings/2020/199.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/199","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":199,"pages":{"first":1431,"last":1437},"keywords":["Natural Language Processing: NLP Applications and Tools","Natural Language Processing: Natural Language Processing","Natural Language Processing: Named Entities","Data Mining: Mining Text, Web, Social Media"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24954/file_24954.m3u8","length_sec":292,"provider":"hls"}],"references":[{"position":1,"raw":"[Cambria et al., 2018] Erik Cambria, Soujanya Poria, Deva- manyu Hazarika, and Kenneth Kwok. 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