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Answer Extraction Based on Merging Score Strategy of Hot Terms

机译：基于热词合并得分策略的答案提取

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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.
机译：答案提取（AE）是开发开放域问答（Q＆A）系统的关键技术之一。它的任务是根据有效的答案评分策略为预期的答案产生最高的分数。我们引入基于热门术语的合并分数策略（MSS）提取答案的方法。热门词汇是根据其词汇和句法特征定义的，以突出疑问词的作用。为了应对语料库的句法多样性，我们提出了四种改进的候选答案评分算法。它们中的每一个都基于热词的词汇功能以及它们与候选答案的句法关系。提出了两种独立的语料库评分算法来挖掘语料库在对候选答案进行排名中的作用。 MSS采用了六种算法来挖掘语料库，候选答案和问题之间的互补作用。实验证明了该策略的有效性。

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