An answer extraction method of a community question and answer system

Abstract

The invention relates to an answer extraction method for a community question and answer system. The method comprises the following steps: firstly, carrying out word segmentation and stop word removalpreprocessing on a question and answer data set; selecting a word meaning similarity scheme, and setting a threshold value to execute normalization of synonymous words and synonymous words; calculating smooth inverse frequency similarity based on question classification and public component removal, sorting the smooth inverse frequency similarity, and selecting k candidate questions with closestsemantics; calculating the similarity of the k candidate questions by considering the character level characteristics and the dependency pyramid characteristics, namely, based on the character vectorcalculation similarity, sorting the candidate questions, and sorting the candidate questions according to the comprehensive dependency similarity; and finally, selecting an optimal answer by comprehensively balancing the ranking and numerical values. A problem classification strategy is adopted, and the range of subsequent calculation is narrowed; public components of the data set are removed at the sentence level, and the smooth inverse frequency similarity is adopted as a sorting reference to accurately screen the first k candidate problems; the questions with the closest semantics are moreefficiently and accurately determined in the community question and answer data, and the efficiency and accuracy of answer extraction are improved.

Description

technical field [0001] The invention relates to an answer extraction method of a community question answering system, belonging to the technical field of automatic question answering. Background technique [0002] Automatic question answering is the task of using computers to automatically answer questions raised by users to meet user knowledge needs. According to different target data sources, it can be divided into three categories: retrieval question answering, community question answering and knowledge base question answering. When answering user questions, the automatic community question answering system needs to correctly understand the natural language questions raised by users, extract the key semantic information, and then obtain the answers through retrieval, matching, and reasoning in the existing corpus, knowledge base, or question answering base. and return to the user. [0003] For community Q&A, the core problem is to find historical questions that are seman...

AI Technical Summary

Problems solved by technology

However, most text similarity methods are mainly aimed at declarative sentences. When the sentence length is long and the components are complete, the effect is acceptable, but the effect is not good when calculating the similarity between short questions. Therefore, most text similarity methods Not suitable for community Q&A system answer extraction

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