Research And Application Of Text Semantic Matching Model Based On Cross-knowledge Enhancement

Text matching is a fundamental task in natural language processing that has extensive applications in information retrieval,text mining and other fields.In practical applications,text matching still faces many challenges.The existing mainstream text matching models usually suffer from issues such as polysemy and inaccurate capture of semantic information,which leads to the inability to effectively extract sentence context and implicit semantic information and low accuracy.To solve the above problems,this paper proposes a cross-knowledge enhanced text semantic matching model,which is based on global-local cross-knowledge enhancement and fine-grained cross-knowledge enhancement for semantic matching method implementation.The research presented in this paper focuses on three main areas.(1)To address the problem of polysemy in text,this paper proposes a semantic matching method based on global-local cross-knowledge enhancement.Firstly,the method embeds the text from the word granularity and introduces the How Net external knowledge base,using the semantic knowledge contained in the knowledge base to enrich the semantic information of the words.Secondly,the method uses the gate recurrent unit and bidirectional gated recurrent unit to encode sentences from word granularity,capturing the semantic information hidden in the text.Thirdly,the method combines multi-head attention mechanisms and convolutional neural networks to perform inter-sentence word interaction from both global and local aspects,capturing the hidden semantic information at a deeper level.Finally,pools are used to extract global and critical information of sentences to predict the semantic similarity of the sentence pairs.The experiments show that the proposed global-local cross-knowledge enhancement method outperforms traditional text matching methods,solving the problem of polysemy and enriching the semantic information of words.These results prove the effectiveness of the proposed method.(2)To address the problem of inaccurate capture of semantic information in text matching,this paper proposes a fine-grained cross-knowledge enhanced semantic matching method.Firstly,the method improves the word vectors,uses fine-grained character vectors and word vectors with position information to capture the deep semantic information of the sentence,and introduces it with the How Net external knowledge base.Secondly,the method uses the bidirectional gated recurrent unit,bidirectional long short-term memory network and self-attention mechanism to encode sentences from character and word granularity,capturing the semantic information hidden in characters and words.Thirdly,the method uses the multi-head attention mechanism and convolutional neural networks for intra-sentence and inter-sentence interactions.The method extracts the dependencies of characters and words in different semantic spaces.Finally,the method adopts maximum and average pooling to extract the global and critical features of the text,and the feature representation of the sentence is obtained to predict whether the two sentences are similar.The experiments show that the fine-grained cross-knowledge enhanced semantic matching method proposed in this paper captures the fine-grained semantic features of text and effectively improves the accuracy of text semantic matching.(3)To validate the effectiveness of the cross-knowledge enhanced text-semantic matching model,this paper applies the model to the intelligent marking of a smart online education platform,and models the intelligent marking process to realize the intelligent marking function of subjective questions,which proves the effectiveness of the cross-knowledge enhanced text-semantic matching model to solve practical problems.