Question classification method and question classification device for automatic question-answering system

Abstract

The invention provides a question classification method and a question classification device for an automatic question-answering system. A class reference table including fine granularity classes of noun and interrogative pronoun is preset. The method comprises the steps: calling a preset interface for executing the class marking operation to divide a received question into a plurality of word segmentations, and carrying out the fine granularity class marking for the word segmentations according to the class reference table to obtain a corresponding fine granularity class mark sequence; matching the fine granularity class mark sequence with a preset first-level classification mode so as to primarily determine the type of the question; if the primarily determined type is non-unique, matching the fine granularity class mark sequence with a preset second-level classification mode so as to secondarily determine the type of the question; if the secondarily determined type is non-unique, matching the fine granularity class mark sequence with a preset third-level classification mode so as to determine the type of the question at a third time, and determining the type to be the type of the question if the type determined at the third time is unique. By adopting the method, the question classification efficiency and accuracy can be improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a question classification method and device applied to an automatic question answering system. Background technique [0002] Automatic question answering system, also known as QA (Question Answering) system, can be called a new generation of search engine. Users do not need to decompose their questions into keywords, but can directly submit the whole question to the system, and can use natural language sentences Asking questions can also directly return answers for users, which can better meet the user's retrieval needs. [0003] Question answering systems are classified from the perspectives of knowledge domains and answer sources, and can be divided into closed domain automatic question answering systems and open domain automatic question answering systems. Among them, the open-domain question answering system has received more and more attention because of its ability t...

AI Technical Summary

Problems solved by technology

[0007] The first method, based on pattern matching, refers to extracting information such as words, semantics, and syntactic structure in the question sentence according to the question form on the surface of the question sentence, manually designing the question classification rules, and comparing the question sentence with the pre-defined rules. Matching to determine the answer category, the scalability of this method is poor, and it cannot contain all the classification rules for various questions, so it is difficult to meet the needs of practical applications;

[0008] The second is a method based on machine learning, which involves decision trees, SNOW and support vector machine SVM, etc., wherein the support vector machine SVM has different effects when implementing question classification due to the use of different kernel functions, and its effect It is better than decision tree and SNOW, but it also performs poorly in the practical application of question classification;

[0009] The third method is based on syntactic structure analysis. This method has been increasingly used in the field of natural language processing in recent years and has made some progress in research. However, the previous analysis only stayed on the surface of words in questions. , ignoring problems such as the laws existing in the key dependency relationship between the parts of speech marked by words

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