Construction and utilization method for context-aware dynamic word or character vector on the basis of deep learning

Abstract

The invention belongs to the technical field of the natural language processing of computers, in particular to a construction and utilization method for a context-aware dynamic word or character vector on the basis of deep learning. The dynamic construction method for the context-aware dynamic word or character vector on the basis of the deep learning comprises the following steps of: in massive texts, through an unsupervised learning method, simultaneously learning a global feature vector of a word or character and the feature vector representation of the global feature vector when a specific context appears, and combining the global feature vector with the context feature vector, and dynamically generating word or character vector representation. By use of the method, the word or character vector dynamically constructed on the basis of the context can be applied to a natural language processing system. The method is mainly used for solving a problem that the word or character vector expresses different meanings in different contexts, i.e. the problem that one word or one character has multiple meanings can be solved. The dynamic word or character vector can be used for obviously improving the performance of various natural language processing tasks of different languages, wherein the tasks comprise Chinese word segmentation, part-of-speech tagging, naming recognition, grammatical analysis, semantic role tagging, sentiment analysis, text classification, machine translation and the like.

Description

technical field [0001] The invention belongs to the technical field of computer natural language processing, and in particular relates to methods for constructing and using dynamic words or word vectors. Background technique [0002] In recent years, deep learning has made breakthroughs in recent artificial intelligence research. It has ended the situation that artificial intelligence has not made breakthroughs for more than ten years, and has rapidly exerted influence in the industry. Deep learning is different from narrow artificial intelligence systems that can only complete specific tasks (task-oriented functional simulation). As a general artificial intelligence technology, it can deal with various situations and problems. It has been obtained in the fields of image recognition, speech recognition, etc. Extremely successful application, also achieved results in the field of natural language processing (mainly English). Deep learning is currently the most effective and ...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a method for constructing and using a context-aware dynamic word or word vector based on deep learning. such as words in English and characters in Chinese)

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