Research On Methods Of Text Semantic Understanding In Complex Scenes

Natural language semantic understanding aims to enable machines to understand the abstract semantic information expressed by humans through text.As the crystallization of thousands of years of human knowledge and wisdom,text is not only an extremely important carrier of human language,but also one of the most important entry points for machines to understand human language.Therefore,studying the semantic understanding methods of natural language text not only has significant scientific and practical value,but is also a key research topic in exploring the completeness of artificial intelligence.Traditional research on natural language text semantic understanding mainly focuses on single scenarios,limiting the depth and breadth of natural language semantic research in complex scenarios,and still faces many key challenges.For example:In terms of fine-grained text semantic representation,traditional natural language text semantic understanding methods often cannot effectively express the subtle differences in the text,making it difficult to perform more precise text analysis and semantic understanding.In terms of cross-domain text semantic transfer,the language expression and semantic features of different domains vary greatly,and traditional methods have difficulty in effectively transferring and applying semantic knowledge across domains.In terms of text semantic fusion in item recommendation,recommendation systems need to consider multiple factors,such as text content and user interests,so it is necessary to integrate text semantics with other information,but traditional methods have difficulty in achieving effective integration.this dissertation addresses the above issues,focusing on the text semantic understanding methods in complex scenarios,conducting systematic research on the features of natural language text in fine-grained,cross-domain,and item recommendation scenarios,and comprehensively improving the text semantic modeling effect in complex scenarios.The main innovations and contributions of this dissertation can be summarized as follows:First,this dissertation studies the text semantic representation method in finegrained scenarios.Traditional text classification research mostly focuses on the overall semantic mining of texts,while in many current application scenarios,natural language texts usually contain various semantic or emotional expressions from people.Therefore,studying the semantic representation of text in fine-grained scenarios is a key aspect of deep understanding of natural language semantics.On the one hand,for the static extraction of text semantics in fine-grained semantic understanding,this dissertation proposes a semantic representation framework based on aspect-aware mechanisms.The framework first uses the different aspects contained within the text and their semantic similarities,and then designs an aspect-aware attention module to extract fine-grained emotional semantic features at the aspect level.A large number of experimental results in aspect-level sentiment analysis tasks have verified the model’s precise predictive performance and strong generalization ability.On the other hand,for the dynamic representation of text semantics in fine-grained semantic understanding,a pre-trained language model based on dynamic weighting networks is proposed.The model designs a lightweight semantic adapter to simulate the entire process of human semantic understanding of natural language texts in new scenarios,and embeds it into large-scale pre-trained models for fine-tuning to achieve effective representation of fine-grained semantics.Extensive experiments on benchmark datasets in the aspect-level sentiment analysis domain have demonstrated the model’s accuracy and interpretability.Second,this dissertation studies the text semantic transfer method in cross-domain scenarios.In cross-domain semantic understanding scenarios,there are widespread issues of low resources and limited annotations.Traditional methods require a large amount of annotated data,resulting in high human and time costs.To explore semantic understanding problems with a small number of manually annotated samples,this dissertation proposes an interactive transfer learning framework based on attention mechanisms.This framework includes a semantic representation module,a semantic transfer module,and a semantic interaction module,and it designs long short-term memory networks to model cross-domain semantic knowledge,thereby solving the text semantic mining,representation,and classification problems in the absence of high-quality and multi-annotated samples.Experiments on cross-domain sentiment semantic analysis datasets demonstrate the model’s classification accuracy and cross-domain stability.Furthermore,to address the issues of low data quality and limited transferable features during the text semantic transfer process,this dissertation proposes an adaptive semantic transfer model based on graph neural networks,starting from the semantic relevance between the internal structures of the text.The model constructs a unique syntactic relationship graph for the text to uniformly represent the internal semantic structure of the text and designs a hybrid graph convolutional network to aggregate the aforementioned syntactic features.Experimental results show that the model significantly improves the text sentiment classification accuracy and utilization of unlabeled data in cross-domain scenarios.Finally,this dissertation studies the text semantic fusion method in item recommendation scenarios.In real item recommendation scenarios,the user interest drift problem refers to the changes in user interests along with time,location,and even user emotions.Efficiently modeling user interests hidden in text information is also a major challenge faced by current recommendation systems.To address the issues of inconsistent heterogeneous data distribution and difficult feature alignment,this dissertation first proposes a semantic fusion framework based on dynamic collaborative mechanisms.The framework mines user item review text information and item attribute information and designs bidirectional recurrent neural networks to model dynamic text semantic features and static item attribute features,allowing the algorithm to achieve efficient fusion in the training optimization process of both and make better use of semantic information in the text,ultimately improving the model’s performance in predicting the success rate of crowdfunding items.Further,to address the impact of text attribute information on item recommendation effectiveness,a semantic enhancement model based on hierarchical interaction networks is proposed.This model uses a multilevel interaction attention network to model fine-grained semantic features such as user text attributes and item text descriptions,and further extends the application scenario to click-through rate prediction tasks to capture the potential relationships between user preferences hidden in the text representation.Experimental results on real datasets in the item recommendation domain show that the model significantly improves item prediction and recommendation effects.