Answer Extraction Based on Merging Score Strategy of Hot Terms  

作　　者：LE Juan ZHANG Chunxia NIU Zhendong 

机构地区：[1]School of Computer Science and Technology,Beijing Institute of Technology [2]School of Software,Beijing Institute of Technology [3]Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application,Beijing Institute of Technology

出　　处：《Chinese Journal of Electronics》2016年第4期614-620,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61370137,No.61272361);the National Basic Research Program of China (973 Program)(No.2012CB720702);the Ministry of Education in China Project of Humanities and Social Sciences(No.13YJC870011)

摘　　要：Answer extraction(AE) is one of the key technologies in developing the open domain Question & answer(Q&A) system.Its task is to yield the highest score to the expected answer based on an effective answer score strategy.We introduce an answer extraction method by Merging score strategy(MSS) based on hot terms.The hot terms are defined according to their lexical and syntactic features to highlight the role of the question terms.To cope with the syntactic diversities of the corpus,we propose four improved candidate answer score algorithms.Each of them is based on the lexical function of hot terms and their syntactic relationships with the candidate answers.Two independent corpus score algorithms are proposed to tap the role of the corpus in ranking the candidate answers.Six algorithms are adopted in MSS to tap the complementary action among the corpus,the candidate answers and the questions.Experiments demonstrate the effectiveness of the proposed strategy.Answer extraction（AE） is one of the key technologies in developing the open domain Question ＆ answer（Q＆A） system.Its task is to yield the highest score to the expected answer based on an effective answer score strategy.We introduce an answer extraction method by Merging score strategy（MSS） based on hot terms.The hot terms are defined according to their lexical and syntactic features to highlight the role of the question terms.To cope with the syntactic diversities of the corpus,we propose four improved candidate answer score algorithms.Each of them is based on the lexical function of hot terms and their syntactic relationships with the candidate answers.Two independent corpus score algorithms are proposed to tap the role of the corpus in ranking the candidate answers.Six algorithms are adopted in MSS to tap the complementary action among the corpus,the candidate answers and the questions.Experiments demonstrate the effectiveness of the proposed strategy.

关 键 词：Question ＆ answer（Q＆A） Answer extraction（AE） Merging score strategy（MSS） Hot terms 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]