Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.
The 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.
1 Rensselaer Polytechnic Institute , Troy, New York.
Lifu
L
Huang
2 Information Sciences Institute , Marina del Rey, California.
Jonathan
J
May
1 Rensselaer Polytechnic Institute , Troy, New York.
Xiaoman
X
Pan
1 Rensselaer Polytechnic Institute , Troy, New York.
Heng
H
Ji
3 University of Illinois at Urbana-Champaign , Urbana, Illinois.
Xiang
X
Ren
3 University of Illinois at Urbana-Champaign , Urbana, Illinois.
Jiawei
J
Han
4 Research and Technology Center , Robert Bosch LLC, Palo Alto, California.
Lin
L
Zhao
1 Rensselaer Polytechnic Institute , Troy, New York.
James A
JA
Hendler
D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D009323:Natural Language Processing; D012660:Semantics; D057170:Translational Research, Biomedical
Liberal Information Extraction; fine-grained entity typing; multi-level entity mention and representation; unsupervised learning