关键词: 实体关系抽取, 远程监督, 因子图模型, 知识融合

Abstract:

The bidding website resources contain rich potential intelligence information. With the help of knowledge base, distant supervision can automatically annotate data, which makes up for the shortcomings of traditional information extraction methods that rely heavily on manual work in the stage of corpus preparation, and can effectively improve the efficiency of information extraction. However, due to the noise data caused by this method, the effect of information extraction is not ideal. In this paper, a method of distant supervision for entity relation extraction method based on the factor graph model is proposed. Combined with domain characteristics, knowledge fusion technology is used to improve the quality of entity extraction, and then a noise reduction method based on learning from negative case data is proposed to overcome the shortcomings of distant supervision. The experimental results show that the proposed method can effectively reduce the “noise” interference and improve the performance of relation extraction.

Key words: entity relation extraction, distant supervision, factor graph module, knowledge fusion