Text intention matching method oriented to intelligent questions and answers and based on internal correlation coding

Abstract

The invention discloses a text intention matching method oriented to intelligent questions and answers and based on internal correlation coding, and belongs to the field of artificial intelligence. Inorder to solve the technical problem of how to accurately judge whether a text intention is matched or not, the adopted technical scheme is as follows: a text intention matching model consisting of amulti-granularity embedding module, an internal correlation encoding module, a global reasoning module and a label prediction module is constructed and trained to realize deep encoding of informationof different granularities of a text, and meanwhile, a soft alignment attention mechanism is used for obtaining internal correlation information between different granularities; a representation of the text and a multi-granularity representation between the texts are generated through global maximum pooling and global average pooling; similarity calculation is performed on the representations ofthe two texts, and a similarity calculation result is combined with the multi-granularity representation between the texts to obtain a final interaction information representation of the text pair; and the text pair intention matching degree is calculated to achieve the purpose of judging whether the text pair intention is matched or not.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and natural language processing, in particular to a text intent matching method based on internal correlation coding for intelligent question answering. Background technique [0002] As a branch of text semantic matching, text intent matching is crucial for intelligent question answering systems. In an intelligent question answering system, text intent matching is used to judge whether two texts have similar intents and whether they can be answered with the same answer. Textual intent matching is a very challenging task, which has not been fully addressed by existing methods. [0003] Existing methods usually simply consider the literal features of the text at the sentence level, while ignoring the deep semantic features. Some works try to extract deep semantic features through the attention mechanism. Although these works have obtained some deep semantic features from different...

AI Technical Summary

Problems solved by technology

Some works try to extract deep semantic features through the attention mechanism. Although these works have obtained some deep semantic features from different angles, they are generally troubled by the lack of features in the deep encoding process.

In particular, in the face of the diversity and complexity of Chinese semantics, it is difficult for existing methods to fully capture the complex and deep semantic features contained in the text.

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