Show simple item record

dc.contributor.authorHuang, Lifu
dc.contributor.authorMay, Jonathan
dc.contributor.authorPan, Xiaoman
dc.contributor.authorJi, Heng
dc.contributor.authorRen, Xiang
dc.contributor.authorHan, Jiawei
dc.contributor.authorZhao, Lin
dc.contributor.authorHendler, James A.
dc.identifier.citationLifu Huang, Jonathan May, Xiaoman Pan, Heng Ji, Xiang Ren, Jiawei Han, Lin Zhao, and James A. Hendler. Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems. Big Data.Mar 2017.19-31.
dc.description.abstractThe ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.en_US
dc.publisherMary Ann Liebert, Inc.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.titleLiberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systemsen_US

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States