A Boundary Assembling Method for Chinese Entity-Mention Recognition

A boundary assembling (BA) method is presented for Chinese entity-mention recognition. Given a sentence, instead of recognizing entity mentions in a unitary style, the authors' BA method first detects boundaries of entity mentions and then assembles detected boundaries into entity-mention candidates. Each candidate is further assessed by a classifier trained on nonlocal features. This method can make better use of nonlocal features and effectively recognize nested entity mentions. Using the ACE 2005 Chinese corpus, the authors' experimental results show an improvement over state-of-the-art techniques, outperforming existing methods in F-score by 5 percent for entity-mention detection and 4.23 percent for entity-mention recognition.