Paper Info
Title
Multi-prototype Chinese Character Embedding.
Abstract
Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to learn multi-prototype Chinese character embeddings, and explore the usefulness of such character embeddings to Chinese NLP tasks. Evaluation on character similarity shows that multi-prototype embeddings are significantly better than a single-prototype baseline. In addition, used as features in the Chinese NER task, the embeddings result in a 1.74% F-score improvement over a state-of-the-art baseline.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
embedding,multi-prototype,Chinese character
Field
DocType
Citations 
Embedding,Computer science,Speech recognition,Natural language processing,Artificial intelligence
Conference
2
PageRank 
References 
Authors
0.37
16
3
Name
Order
Citations
PageRank
Yanan Lu1974.02
Yue Zhang21364114.17
Donghong Ji3892120.08