A Word Sense Disambiguation Method Based on Multiple Sense Graph

Liu Wenjun, Wang Hailan, Wang Xiping, Hou Mengshu
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:40-47, 2024.

Abstract

Word Sense Disambiguation is a process to determine the best meaning of an ambiguous word according to its contextual semantic information. Many methods ofWord Sense Disambiguation cannot deal with polysemous words well because they only consider the meaning of the adjacent words before and after ambiguous words, and cannot consider the meaning of all words in the sentence globally. In order to solve the above problems, this paper proposes a word sense disambiguation method based on Multiple Sense Graph. This method applies the BERT model to generate word sense vectors, and globally considers the feature relationship between the ambiguous word and all words in the context. In addition, this method applies the PageRank algorithm to score the importance of each sense vector of the word, and the scoring results are sorted to obtain the best sense of the ambiguous word. The experimental results indicate that the proposed BERT-PageRank method improves the evaluation index compared with the other two semantic disambiguation methods. In summary, the proposed method improves the accuracy of word sense disambiguation to obtain the best word sense.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-wenjun24a, title = {A Word Sense Disambiguation Method Based on Multiple Sense Graph}, author = {Wenjun, Liu and Hailan, Wang and Xiping, Wang and Mengshu, Hou}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {40--47}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/wenjun24a/wenjun24a.pdf}, url = {https://proceedings.mlr.press/v245/wenjun24a.html}, abstract = {Word Sense Disambiguation is a process to determine the best meaning of an ambiguous word according to its contextual semantic information. Many methods ofWord Sense Disambiguation cannot deal with polysemous words well because they only consider the meaning of the adjacent words before and after ambiguous words, and cannot consider the meaning of all words in the sentence globally. In order to solve the above problems, this paper proposes a word sense disambiguation method based on Multiple Sense Graph. This method applies the BERT model to generate word sense vectors, and globally considers the feature relationship between the ambiguous word and all words in the context. In addition, this method applies the PageRank algorithm to score the importance of each sense vector of the word, and the scoring results are sorted to obtain the best sense of the ambiguous word. The experimental results indicate that the proposed BERT-PageRank method improves the evaluation index compared with the other two semantic disambiguation methods. In summary, the proposed method improves the accuracy of word sense disambiguation to obtain the best word sense.} }
Endnote
%0 Conference Paper %T A Word Sense Disambiguation Method Based on Multiple Sense Graph %A Liu Wenjun %A Wang Hailan %A Wang Xiping %A Hou Mengshu %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-wenjun24a %I PMLR %P 40--47 %U https://proceedings.mlr.press/v245/wenjun24a.html %V 245 %X Word Sense Disambiguation is a process to determine the best meaning of an ambiguous word according to its contextual semantic information. Many methods ofWord Sense Disambiguation cannot deal with polysemous words well because they only consider the meaning of the adjacent words before and after ambiguous words, and cannot consider the meaning of all words in the sentence globally. In order to solve the above problems, this paper proposes a word sense disambiguation method based on Multiple Sense Graph. This method applies the BERT model to generate word sense vectors, and globally considers the feature relationship between the ambiguous word and all words in the context. In addition, this method applies the PageRank algorithm to score the importance of each sense vector of the word, and the scoring results are sorted to obtain the best sense of the ambiguous word. The experimental results indicate that the proposed BERT-PageRank method improves the evaluation index compared with the other two semantic disambiguation methods. In summary, the proposed method improves the accuracy of word sense disambiguation to obtain the best word sense.
APA
Wenjun, L., Hailan, W., Xiping, W. & Mengshu, H.. (2024). A Word Sense Disambiguation Method Based on Multiple Sense Graph. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:40-47 Available from https://proceedings.mlr.press/v245/wenjun24a.html.

Related Material