Word Sense Disambiguation Model based on Bi-LSTM

• Published in: International Conference on Measuring Technology and Mechatronics Automation (Print) pp. 848 - 851
• Main Author: Zhang, Liwei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2022
• Subjects: attention mechanism Introduction; Automation; Mechatronics; Natural language processing; Neural networks; recurrent neural network; Semantics; Training; Vocabulary; word sense disambiguation; word vector
• ISSN: 2157-1481
• DOI: 10.1109/ICMTMA54903.2022.00172

Word sense disambiguation is a very important task in natural language processing, and it is also a basic work in this field. There are many polysemous words in Chinese vocabulary. Using the word sense disambiguation model can determine the correct meaning of polysemous words in different contexts according to the context of polysemous words, so as to eliminate the ambiguity caused by polysemous words in Chinese. This paper proposes a neural sequence learning model based on Bi-LSTM (bidirectional long short memory) to realize word sense disambiguation. The word vector of the sentence is input into the neural network to train the similarity model, and the different sense items are classified, and the ambiguity is determined by the similarity comparison. The correct sense item of the word thus realizes the disambiguation of the word sense. Experiments show that the disambiguation model proposed in this paper has higher disambiguation accuracy than traditional methods.