An Attention Word Sense Disambiguation Model Based on Multi-Channel Residual Hybrid Dilated Convolution
Aiming at the insufficient generalization ability of the current word sense disambiguation model,multi-channel residual hybrid dilated convolution with attention word sense disambiguation model is proposed to improve disambiguation accuracy and provide help for more tasks of natural language processing.Linguistic knowledge is used to construct disambiguation features,three vectorization methods are employed to vectorize the disambiguation features to form a three-channel word embedding matrix,and positional coding is deeply fused with the three-channel word embedding matrix.A complex convolutional encoder is designed to increase the expressive power of the model.Experiments are conducted on word sense disambiguation datasets SemEval-2007:Task#5 and SemEval-2021:Task#2 in tasks of semantic evaluation,and the results show that compared to the latest word sense disambiguation using clustered sense labels and multi-head attention mechanism,the average bias of the proposed method is reduced to 1.345％ and 2.157％ respectively,which effectively improves the performance of word sense disambiguation.
word sense disambiguationlinguistic knowledgehybrid dilated convolutionconvolutional encoder
NETL
NSTL
万方数据
