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Question and answer sentence similarity calculation method based on multilevel characteristics

A technology of sentence similarity and calculation method, applied in the direction of calculation, special data processing applications, instruments, etc., can solve problems such as inability to comprehensively measure similarity, and insufficient expression of word and syntactic similarity.

Active Publication Date: 2017-08-01
ZHEJIANG UNIV
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Problems solved by technology

This method can measure the semantic similarity of sentences, but the similarity of words and syntax cannot be fully expressed
[0011] Traditional methods of calculating sentence similarity have their own advantages and disadvantages, and none of them can comprehensively measure the similarity between two sentences.

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  • Question and answer sentence similarity calculation method based on multilevel characteristics
  • Question and answer sentence similarity calculation method based on multilevel characteristics
  • Question and answer sentence similarity calculation method based on multilevel characteristics

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Embodiment Construction

[0059] The present invention will be further described in detail below in conjunction with accompanying drawings and examples.

[0060] Such as figure 1 As shown, the method for measuring the similarity between question and answer sentences proposed by the present invention includes five feature functions, and each feature function is used to measure the similarity between two sentences from different angles. Among them, the feature at the word level is to calculate the similarity of two sentences in terms of words starting from words. Phrase-level features can deal with local context-dependent problems, and can match well with idioms and common words. Sentence semantic features measure the similarity of two sentences in terms of meaning. Sentence structure features measure the grammatical and syntactic similarity of two sentences. The answer type feature can measure whether the answer sentence contains the answer type required by the question. Finally, the five feature fu...

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Abstract

The invention discloses a question and answer sentence similarity calculation method based on multilevel characteristics, and is mainly applied to an automatic question-answering system. The method totally comprises five characteristic functions, wherein each characteristic function is independently used for measuring a similarity among different angles between two sentences; a word-level characteristic measures the similarity of two sentences on an aspect of words; a phrase-level characteristic can measure a similarity between an idiom and a phrase in common use; a sentence semantic characteristic can measure the similarity of the two sentences on an aspect of meanings; a sentence structure characteristic can measure the similarity of the two sentences on an aspect of grammar and syntax; an answer type characteristic can measure whether a required answer type is contained in an answer sentence; and finally, through a linear function, the five characteristic functions are subjected to weighted summation to form a question and answer sentence similarity algorithm based on multilevel characteristics. By use of the method, the similarity between a question sentence and an answer sentence can be comprehensively measured.

Description

technical field [0001] The invention relates to the technical field of automatic question answering, in particular to a method for calculating the similarity between question sentences and answer sentences. Background technique [0002] An automatic question answering system is a system that can directly return accurate answers after inputting natural language questions. According to the type of data source behind the automatic question answering system, it can be divided into two categories, namely the automatic question answering system based on knowledge graph and the automatic question answering system based on unstructured documents. The data source of an automatic question answering system based on unstructured documents is a large number of unstructured text documents, and the process of retrieving answers mainly includes the following steps: [0003] (1) According to the keyword matching algorithm, the paragraphs related to the question are retrieved. [0004] (2) ...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/3329G06F16/367
Inventor 王东辉徐灿庄越挺
Owner ZHEJIANG UNIV
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