Text classification method combining dynamic word embedding with part-of-speech tagging

Abstract

The invention discloses a text classification method combining dynamic word embedding with part-of-speech tagging, and provides the text classification method based on a deep neural network through combining dynamic word embedding with part-of-speech tagging. The method can fully utilize the advantages that a large-scale corpus can provide more accurate grammar and semantic information, and can also adjust word embedding by combining with the features of the corpus during a model training process, and thus the features of the corpus can be better learned. Meanwhile, classification accuracy can be further improved by combining with part of speech information of words in sentences. The invention also comprehensively utilizes the advantages of LSTM in the aspect of learning context information of words and part of speech in the sentences, and the advantages of CNN in the aspect of learning text local features. The classification model provided by the invention has the advantages of high accuracy and strong universality, and achieves good effect in some famous public corpuses including IMDB corpus, Movie Review and TREC.

Description

technical field [0001] The invention relates to the technical field of mobile communication. Specifically, it relates to a text classification method combining dynamic word embedding and part-of-speech tagging. Background technique [0002] Automatic text classification based on machine learning refers to the process of using various computer algorithms to analyze the content of text and automatically determine the text category under the premise of a given classification system. Early research was mainly based on shallow machine learning and statistics, and used one-hot (also known as one-of-V, V is the size of the dictionary) or distributional methods (such as combining word frequency, co-occurrence information, TF- IDF or bag-of-words for entropy) to give a mathematical representation of a sentence. The main disadvantage of this representation method is that it cannot express the semantics of language units (such as words, words or phrase n-grams) in sentences and the r...

AI Technical Summary

Problems solved by technology

However, these studies mainly use static word embedding, that is, the value of each vector element in the word embedding remains unchanged during the model training process, which makes the model unable to better learn the characteristics of the text in the target corpus

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