A concept-based semantic recognition method and device

Abstract

The invention discloses a concept-based semantic identification method and device. The method comprises the following steps: step S1, carrying out word segmentation on a text to be segmented, and obtaining a plurality of segmented strings; step S2, matching the segmented character string with all nodes of the semantic web; step S3, performing word sense disambiguation on the string successfully matched with the node of the semantic web to obtain a concept path of the disambiguated string, and storing the concept path in a word concept path storage library; step S4, outputting the concept pathof the related words in the concept path storage library of the text to be segmented. The invention can effectively improve the accuracy rate and calculation speed of the text semantic concept recognition.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a concept-based semantic recognition method and device. Background technique [0002] At present, the mainstream Chinese word segmentation technology mainly has the following two types: [0003] One is the mechanical word segmentation method (based on the dictionary). The principle of mechanical word segmentation is to match the strings in the document with the entries in the dictionary one by one. If a string is found in the dictionary, the match is successful and can be segmented. Otherwise, it will not be segmented, and the mechanical word segmentation method based on the dictionary is simple and practical; [0004] The second is the word segmentation method based on grammar and rules. Its basic idea is to perform syntactic and semantic analysis at the same time as word segmentation, and use syntactic information and semantic information to perform part-of...

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Problems solved by technology

[0006] 1. The biggest disadvantage of mechanical word segmentation is that the completeness of the dictionary cannot be guaranteed

First of all, the entry of words in the dictionary has a time lag. Secondly, the words entered in the dictionary cannot cover all proper nouns such as names of people, places, and institutions. According to statistics, most of the words entered in the dictionary have names, places, and institutions. proper noun;

[0007] 2. Word segmentation based on grammar and rules Because the existing grammatical knowledge and syntactic rules are very general and complex, the accuracy achieved by word segmentation based on meta and rules is far from satisfactory

Therefore, this word segmentation system is still in the experimental stage.

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