Text file word sense disambiguation method and device

Abstract

The invention discloses a word sense disambiguation method. The text file word sense disambiguation method comprises the steps that multiple reference text contents with determined word senses are configured; at least one text file to be disambiguated is obtained; the text contents are extracted from the text file according to each text file to be disambiguated and are subjected to word segmentation processing so as to obtain a first word set and determine words to be disambiguated in the first word set, at least one reference text contents corresponding to the words to be disambiguated are extracted and are subjected to word segmentation processing so as to obtain at least one second word set, correlation values between the text files and the reference text contents are calculated based on the first word set and the second word sets, and it is determined that the text files are correlated with the reference text contents having the highest correlation values; the text files to be disambiguated are put in a word sense category corresponding to the correlated reference text contents. The invention further discloses a corresponding device. The method and the device can improve the disambiguation efficiency.

Description

technical field [0001] The present application relates to the field of information technology (IT, Information Technology), in particular to a method and device for word sense disambiguation of text files. Background technique [0002] Word Sense Disambiguation (Word Sense Disambiguation, WSD) is an important research topic in the field of computational linguistics and natural language processing. The accuracy of the disparity results will directly affect the processing results of these technologies. [0003] Word sense disambiguation techniques can be classified into supervised and unsupervised. Supervised word sense disambiguation technology needs to annotate the corpus according to the meaning of the word, and use machine learning technology to train the classifier to determine the meaning of the new instance. However, this method relies on labeled data, and the acquisition of labeled data consumes a lot of labor costs, making this method difficult Applied to large-scal...

AI Technical Summary

Problems solved by technology

Supervised word sense disambiguation technology needs to annotate the corpus according to the meaning of the word, and use machine learning technology to train the classifier to determine the meaning of the new instance. However, this method relies on labeled data, and the acquisition of labeled data consumes a lot of labor costs, making this method difficult Applied to large-scale word sense disambiguation tasks

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