Sense-Aware Retrieval-Based Question Answering via Word Ambiguity Induction
To solve the problem of inconsistent semantic expression of polysemous words in different contexts,we propose a sense-aware question-answer model.During the semantic matching process of questions and candidate answers,the model integrates with external knowledge sources to dynamically identify and detect the semantics of each polysemous word in different scenarios.The detected semantic information is encoded as features and then integrated into the semantic matching task,enabling the model to capture the exact meaning of each word and achieve better matching performance.In the design of the ambiguity perception model,we adopt a deep semantic encoder based on the Transformer,which enables it to capture more comprehensive depth semantic features of the analyzed ambiguous words and knowledge sources,making more accurate semantic disambiguation.Experimental results on standard retrieval-based Q&A datasets(WikiQA and TrecQA)demonstrate that the proposed sense-aware Q&A method can effectively be integrated into multiple baseline models,capturing the precise semantics of polysemous words in different contexts.This approach achieves a MAP evaluation performance improvement of approximately 1％compared to corresponding baselines on public datasets.Moreover,this semantic feature enables a BERT-based text matching approach to outperform other state-of-the-art models.
NETL
NSTL
万方数据
维普
