Omni-word Feature and Soft Constraint for Chinese Relation Extraction
Yanping Chen, Qinghua Zheng, Wei Zhang · 2014
Chinese is an ancient hieroglyphic. It is inat-tentive to structure. Therefore, segmenting and parsing Chinese are more difficult and less accurate. In this paper, we propose an Omni-word feature and a soft constraint method for Chinese relation extraction. The Omni-word feature uses every potential word in a sentence as lexicon feature, reducing errors caused by word segmentation. In order to utilize the structure information of a relation instance, we discuss how soft constraint can be used to cap-ture the local dependency. Both Omni-word feature and soft constraint make a better use of sentence information and minimize the in-fluences caused by Chinese word segmenta-tion and parsing. We test these methods on the ACE 2005 RDC Chinese corpus. The re-sults show a significant improvement in Chi-nese relation extraction, outperforming other methods in F-score by 10 % in 6 relation types and 15 % in 18 relation subtypes. 1