Username   Password       Forgot your password?  Forgot your username? 

 

Word Sense Disambiguation based on Maximum Entropy Classifier

Volume 15, Number 5, May 2019, pp. 1491-1498
DOI: 10.23940/ijpe.19.05.p26.14911498

Chunxiang Zhanga, Xuesong Zhoub, Xueyao Gaob, and Bo Yua

aSchool of Software and Microelectronics, Harbin University of Science and Technology, Harbin, 150080, China
bSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

(Submitted on December 12, 2018; Revised on January 16, 2019; Accepted on February 18, 2019)

Abstract:

Word sense disambiguation (WSD) is one of the most important research issues in the field of natural language processing. In this paper, a new method of word sense disambiguation is proposed, in which words and parts of speech (POS) are extracted as discriminative features. At the same time, a maximum entropy classifier is adopted to determine ambiguous words' semantic categories. Training data of SemEval-2007: Task#5 is used to optimize the maximum entropy model. A test corpus is applied to test the performance of the WSD classifier. Experimental results show that the performance of word sense disambiguation is improved after the proposed approach is used.

References: 18

    1. Y. Lin, X. Zhu, Z. Zheng, Z. Dou, and R. Zhou, “The Individual Identification Method of Wireless Device based on Dimensionality Reduction and Machine Learning,” Journal of Supercomputing, No. 5, pp. 1-18, 2017
    2. Y. Lin, C. Wang, J. X. Wang, and Z. Dou, “A Novel Dynamic Spectrum Access Framework based on Reinforcement Learning for Cognitive Radio Sensor Networks,” Sensors, Vol. 16, No. 10, pp. 1-22, 2016
    3. Y. Lin, C. Wang, C. Ma, Z. Dou, and X. Ma, “A New Combination Method for Multisensor Conflict Information,” Journal of Supercomputing, Vol. 72, No. 7, pp. 2874-2890, 2016
    4. B. Krawczyk and B. T. McInnes, “Local Ensemble Learning from Imbalanced and Noisy Data for Word Sense Disambiguation,” Pattern Recognition, Vol. 78, No. 1, pp. 103-119, 2018
    5. E. A. Corrêa, A. A. Lopes, and D. R. Amancio, “Word Sense Disambiguation: A Complex Network Approach,” Information Sciences, Vol. 442-443, No. 1, pp. 103-113, 2018
    6. S. Vij, A. Jain, D. Tayal, and O. Castillo, “Fuzzy Logic for Inculcating Significance of Semantic Relations in Word Sense Disambiguation using a WordNet Graph,” International Journal of Fuzzy Systems, Vol. 20, No. 2, pp. 444-459, 2018
    7. W. Alsaeedan, M. E. B. Menai, and S. Al-Ahmadi, “A Hybrid Genetic-Ant Colony Optimization Algorithm for the Word Sense Disambiguation Problem,” Information Sciences, Vol. 417, No. 1, pp. 20-38, 2017
    8. Y. J. Choi, J. Wiebe, and R. Mihalcea, “Coarse-Grained +/- Effect Word Sense Disambiguation for Implicit Sentiment Analysis,” IEEE Transactions on Affective Computing, Vol. 8, No. 4, pp. 471-479, 2017
    9. Y. Gutiérrez, S. Vázquez, and A. Montoyo, “Spreading Semantic Information by Word Sense Disambiguation,” Knowledge-based Systems, Vol. 132, No. 1, pp. 47-61, 2017
    10. H. S. Arora, S. Bhingardive, and P. Bhattacharyya, “Detecting Most Frequent Sense using Word Embeddings and BabelNet,” in Proceedings of the 8th Global WordNet Conference, pp. 22-26, 2016
    11. S. D. K. Sharma, “Hindi Word Sense Disambiguation using Cosine Similarity,” in Proceedings of the International Conference on Information and Communication Technology for Sustainable Development, pp. 801-888, 2016
    12. F. M. Cecchini, E. Fersini, and E. Messina, “Word Sense Discrimination on Tweets: A Graph-based Approach,” in Proceedings of the 7th International Joint Conference on Knowledge Discovery, pp. 138-146, 2015
    13. J. Wang, M. F. Liao, R. F. Hu, and S. H. Wang, “The Word Sense Annotation based on Corpus of Teaching Chinese as a Second Language,” in Proceedings of the 16th Workshop on Chinese Lexical Semantics Workshop, pp. 234-243, 2015
    14. D. J. Choi, M. Hwang, B. Ko, S. You, and P. Kim, “Low Ambiguity First Algorithm: A New Approach to Knowledge-based Word Sense Disambiguation,” in Proceedings of the 2nd International Conference on HCI in Business, pp. 565-574, 2015
    15. R. Jose and V. S. Chooralil, “Prediction of Election Result by Enhanced Sentiment Analysis on Twitter Data using Word Sense Disambiguation,” in Proceedings of the International Conference on Control, Communication and Computing, pp. 638-641, 2015
    16. X. M. Jiang, L. R. Qiu, and Y. Q. Li, “A Tibetan Word Sense Disambiguation Method based on Hownet and Chinese-Tibetan Parallel Corpora,” in Proceedings of the International Standard Conference on Trustworthy Computing and Services, pp. 152-159, 2015
    17. J. Y. Duan, Y. Fu, and X. Li, “Attribute Knowledge Mining for Chinese Word Sense Disambiguation,” in Proceedings of the International Conference on Asian Language Processing, pp. 73-77, 2015
    18. M. A. S. Cabezudo, N. L. S. Palomino, and M. R. Perez, “Improving Subjectivity Detection for Spanish Texts using Subjectivity Word Sense Disambiguation based on Knowledge,” in Proceedings of the 41st Latin American Computing Conference, pp. 53-60, 2015

     

    Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

     
    This site uses encryption for transmitting your passwords. ratmilwebsolutions.com