Answer extraction based on merging score strategy of hot terms

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.