首页|Multi-granularities capture interaction information for text matching accuracy enhancement
Multi-granularities capture interaction information for text matching accuracy enhancement
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NETL
NSTL
万方数据
维普
Modeling and matching texts is a critical issue in natural language processing (NLP) tasks.In order to improve the accuracy of text matching,multi-granularities capture matching features (MG-CMF) model was proposed.The proposed model used convolution operations to construct the representation of text under multiple granularities,used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities.Then,the convolution neural network (CNN) was used to capture the matching information in each granularity.Finally,the captured matching features were input into the fully connected neural network to obtain the matching similarity.By making some experiments,the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.
representation modelinteraction modeltext matchingmulti-granularitycapture the matching information
Cao Xiaopeng、Jin Liang
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School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
NETL
NSTL
万方数据
维普
