Answer selection-oriented multi-angle attention feature matching method and system

Abstract

The invention relates to an answer selection-oriented multi-angle attention feature matching method and system, and the method comprises the steps: using a hybrid coding layer, enriching text features through employing the hybrid coding layer, extracting serialized information in combination with bidirectional LSTM, and obtaining wider short text information through employing convolution kernels of different heights of TextCNN, thereby achieving a complementary effect; using multi-level similarity calculation, extracting local interaction information and overall similarity features between two sentences by using multi-level similarity calculation and through an attention mechanism, then combining with the extracted features, and carrying out similarity scoring by considering local and overall sentences. In the technical scheme, from candidate answers, target answers can be automatically obtained efficiently and accurately.

Description

technical field [0001] The invention relates to an answer selection-oriented multi-angle attention feature matching method and system, belonging to the technical field of natural language processing. Background technique [0002] In the question answering system, after information retrieval, the content of the question answering system needs to sort the candidate answers so as to select the best answer. In scenarios such as community Q&A and intelligent customer service, it is more convenient and efficient to select the appropriate answer from the existing candidate answers than to generate the required answer. Answer selection is a very important task in natural language processing, and it is also an indispensable and important component of question answering systems. The answer selection task can be regarded as an answer matching task. Answer selection tasks are also considered as an application of text matching. However, the retrieval question answering system also fac...

AI Technical Summary

Problems solved by technology

However, the retrieval question answering system also faces the following problems: (1) The question information is scarce

[0005] (1) The method based on feature engineering requires artificial feature engineering. Such methods often have complex feature engineering, and the model can only adapt to specific data sets and is not universal.

Moreover, the effect of the model depends on the design of feature engineering, and the role of people is too important

[0006] (2) Among deep learning-based methods, representation-based methods usually perform relatively independent feature extraction on two sentences. Even if interaction information is added, it is a single-level interaction, and the interaction information is not fully utilized; interaction-based methods are usually difficult to consider Comprehensive interactive information, unable to consider local and global similarities and combine them effectively

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