Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks

• Main Authors: Peng-Hsuan Li, 李朋軒
• Other Authors: Jane Yung-jen Hsu
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/xbpj78

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === Named Entity Recognition (NER) is an important task which locates proper names in text for downstream tasks, e.g. to facilitate natural language understanding. The problem is often casted from structured prediction of text chunks to sequential labeling of tokens. Such sequential approaches have achieved high performance with models like conditional random fields and recurrent neural networks. However, named entities should be linguistic constituents, and sequential token labeling neglects this information. In the thesis, we propose a constituency-oriented approach which fully utilizes linguistic structures in text. First, to leverage the prior knowledge of hierarchical phrase structures, we generate parses and alter them into constituency graphs that minimize inconsistencies between parses and named entities. Then, we use Bidirectional Recursive Neural Networks (BRNN) to propagate relevant structure information to each constituent. We use a bottom-up pass to capture the local information and a top-down pass to capture the global information. Experiments show that this approach is comparable to sequential token labeling, and significant improvements can be seen on OntoNotes 5.0 NER, with F1 scores over 87\%.