Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tags

被引：2

作者：

Xie, Zhipeng ^{[1
]}

机构：

[1] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China

来源：

NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2017 | 2018年 / 10619卷

关键词：

Question answering; Deep learning; Question words; Part-of-speech tags;

D O I：

10.1007/978-3-319-73618-1_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The document-based question answering is to select the answer from a set of candidate sentence for a given question. Most Existing works focus on the sentence-pair modeling, but ignore the peculiars of question-answer pairs. This paper proposes to model the interaction between question words and POS tags, as a special kind of information that is peculiar to question-answer pairs. Such information is integrated into a neural model for answer selection. Experimental results on DBQA Task have shown that our model has achieved better results, compared with several state-of-the-art systems. In addition, it also achieves the best result on NLPCC 2017 Shared Task on DBQA.

引用

页码：136 / 147

页数：12