Method and device for automatically labeling text

Abstract

The invention discloses a method and a device for automatically labeling a text. The method for automatically labeling the text comprises the following steps of identifying vocabularies in the text; labeling identified vocabularies expressing attribute values into formats corresponding to the types which attribute values belong to in a knowledge base; labeling identified notional words into notional knowledge in the knowledge base; on the basis of a result of labeling the notional words, labeling identified pronouns into contents referred to by the pronouns; and on the basis of results of labeling the notional words and the pronouns, labeling identified attribute names into corresponding attribute names in the knowledge base. In the method for automatically labeling the text, which is disclosed by the embodiment of the invention, text is automatically labeled according to the notional knowledge in the knowledge base and the notional knowledge in the knowledge base is deeply integrated, so as to introduce massive structured information in the knowledge base into conventional text processing application and implement reasoning and expansion between the text and the notional knowledge, thereby expanding a very wide application prospect.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for automatically marking text. Background technique [0002] Lexical analysis and processing is a basic technology of NLP (Natural Language Processing, traditional natural language processing), and its main functions include WordSeg (Word Segmentation, natural language text segmentation), PosTag (Part-of-Speech Tagging, part-of-speech tagging) And NER (Named Entity Recognition, proper name recognition). After lexical analysis and processing, the text will be divided into vocabulary forms, and each vocabulary will be given a specific part of speech (for example, verb, noun, adjective, etc.) information. A large number of upper-level application technologies, such as search engine technology, in-depth question answering technology, machine translation technology, etc., are all based on the above analysis results. [0003] However, in the process of realizi...

AI Technical Summary

Problems solved by technology

[0003] However, in the process of realizing the present invention, the inventors found that the prior art has at least the following problems: the lexical analysis only stays on the analysis of the literal semantics of the vocabulary, and for deeper semantic knowledge and the entity meaning represented by the vocabulary, the existing Lexical analysis cannot give more effective information, so subsequent applications based on lexical analysis results cannot be effectively completed, and user experience is poor

Images