Text similarity calculation system and method based on distance perception self-attention mechanism and multi-angle modeling

Abstract

The invention discloses a text similarity calculation system and method based on a distance perception self-attention mechanism and multi-angle modeling, for a text P and a text Q, text similarity calculation is completed by means of Word2vec, BiLSTM, the distance perception self-attention mechanism and multi-angle modeling, firstly Word2ve is used to obtain word representations of the text P and the text Q, and context representations are captured through the BiLSTM; the method comprises the following steps: firstly, extracting a text P and a text Q, then further extracting text features by utilizing a distance-aware self-attention mechanism, then carrying out interactive modeling on the text P and the text Q by utilizing an interactive attention mechanism, and finally, carrying out similarity modeling on the text P and the text Q from different levels by proposing a multi-angle similarity modeling method so as to obtain a final similarity. According to the method, through a self-attention mechanism based on distance perception and a multi-angle similarity modeling architecture, the text semantic modeling capability is enhanced, the accuracy of text similarity calculation is improved, and the model is simple in structure, efficient in performance, high in robustness and easy to popularize and use in practice.

Description

technical field [0001] The invention relates to the technical field of computer text information processing, relates to a method for calculating similarity between texts, and in particular relates to a text similarity calculating system and method based on distance-aware self-attention mechanism and multi-angle modeling. Background technique [0002] In recent years, with the vigorous development of the mobile Internet and the popularization of smart devices, people are more dependent on the Internet in their work and life. As a result, a large number of Internet-based service platforms have emerged, such as search engines, blogs, and intelligent customer service. These services bring convenience to people, but also generate massive data, especially text data, such as query and response data generated when people use search engines, question and answer data between customer service and users, and a large number of similarities in blogs Article data, etc. However, simply rel...

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Problems solved by technology

However, although this type of method captures the alignment relationship between two texts, as the model continues to deepen, when extracting the interaction information between the two texts, the importance of the single semantics of the text is ignored to a certain extent.

[0007] In summary, although the above method can capture the semantic vector of the text, and then calculate the similarity between the texts, it is difficult to extract the deeper semantic features while extracting the interaction information between the two texts. In addition, from various perspectives The similarity measurement of the text vector after feature extraction will also affect the accuracy of the final text to similarity calculation

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