Deep neural network-based text consistency analysis method

Abstract

The invention discloses a deep neural network-based text consistency analysis method. After a section of text is input, a distributed method is adopted to translate each word in sentences into vectorsto form distributed sentence matrices, the words repeatedly appearing in the adjacent sentences are counted, and repeated information of the adjacent sentences is added through a manner of expandingdimensionality of the matrices; a convolutional neural network is utilized to learn distributed representations of the sentences, and important features of logic, semantics, syntax and the like in thesentences are extracted to form sentence vectors; and similarity degrees between the adjacent sentence vectors are calculated to add context association contents, finally, a neural network is continuously trained, and probability of text consistency is output. The method is characterized in that complex artificial feature extraction operations do not need to be carried out, external resources arealso not relied on, and compared with existing consistency analysis technology, the method provided by the invention has a great improvement in an accuracy rate, and has a better practical value.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and relates to a text consistency analysis method based on a deep neural network. Background technique [0002] Text consistency analysis refers to judging whether multiple text sentences are consistent from the perspective of logic and syntax, so that multi-sentence texts are more logically and semantically meaningful, and can be applied to machine translation, question answering systems, and automatic text generation systems . [0003] Existing text consistency research methods are mainly divided into two categories. The first type mainly relies on feature engineering, that is, artificially defining some representative features to capture the logical and syntactic relationships between intersecting sentences, encoding each sentence in the target document into a distinctive feature vector and then comparing these The degree of similarity between features, the degree of simi...

AI Technical Summary

Problems solved by technology

Although recurrent networks can achieve better performance by building convolutions on parse trees, rather than simply accumulating words in sentences like recurrent networks, it is not easy to implement and requires external resources such as parse trees

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