Chinese intelligent question and answer system method based on word similarity of a network platform

Abstract

The invention belongs to the technical field of network natural language processing. The invention discloses a Chinese intelligent question and answer system method based on word similarity of a network platform. In a knowledge base question and answer system, each question and each answer are regarded as two word sets, each word in the question set is matched with each word in the answer set, theword similarity is calculated, then the maximum similarity value is obtained, and then the average value of the maximum value is obtained; The method is simple and high in efficiency; According to the method, the problem of data sparsity of an existing vector included angle cosine method is solved; Meanwhile, the situation of inaccurate answer extraction caused by inconsistent question and answerlanguage structures in the existing mode matching method is also overcome; According to the word similarity algorithm, answers can be found in the knowledge base question and answer system more reasonably and efficiently.

Description

technical field [0001] The invention belongs to the field of intelligent question answering systems for natural language processing, in particular to a Chinese intelligent question answering system method based on word similarity of a network platform. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] The calculation of word similarity is to use a specific value to represent the similarity between two words. It is the main means of understanding the semantic information of words and one of the basic tasks of natural language processing. Word similarity calculation is the main method for semantic understanding, and the solution to the similarity problem will promote the development of related application technologies in the field of natural language processing, such as information retrieval, word sense disambiguation, machine translation, and question answering systems. [0004] Among them, the intellig...

AI Technical Summary

Problems solved by technology

[0018] The disadvantage of this method is that the semantic analysis method relies on supervised data such as for vocabulary tagging and model training. Due to the requirements of data annotation, it can usually only be used in specific fields and requires manual labor. Label a large number of logical expressions for training

[0022] The disadvantage of this existing method is that when calculating classification or calculating similarity, most of them use large-scale corpus for statistical calculation.

[0026] The disadvantage of this method is that this method only considers the statistical frequency of words, but ignores the linguistic meaning of words. Obviously, the similarity calculated based on this method is not accurate; the results obtained according to the statistical method are greatly disturbed by data sparseness. , so there will be obvious calculation errors

[0033] Difficulty: The existing technology needs to label a large amount of manual training data, the situation where the similarity calculation of short texts in the question answering system is not suitable for statistical methods, and the situation where the language structure of the question sentence and the answer is far apart when the pattern matching is far away, and the answer extraction effect is poor

Images