[1] Y. Lin, X. Zhu, Z. Zheng, Z. Dou,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,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,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,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,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,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,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,P. Bhattacharyya, “Detecting Most Frequent Sense using Word Embeddings and BabelNet,” inProceedings of the 8th Global WordNet Conference, pp. 22-26, 2016 [11] S. D. K.Sharma, “Hindi Word Sense Disambiguation using Cosine Similarity,” inProceedings of the International Conference on Information and Communication Technology for Sustainable Development, pp. 801-888, 2016 [12] F. M. Cecchini, E. Fersini,E. Messina, “Word Sense Discrimination on Tweets: A Graph-based Approach,” inProceedings of the 7th International Joint Conference on Knowledge Discovery, pp. 138-146, 2015 [13] J. Wang, M. F. Liao, R. F. Hu,S. H. Wang, “The Word Sense Annotation based on Corpus of Teaching Chinese as a Second Language,” inProceedings of the 16th Workshop on Chinese Lexical Semantics Workshop, pp. 234-243, 2015 [14] D. J. Choi, M. Hwang, B. Ko, S. You,P. Kim, “Low Ambiguity First Algorithm: A New Approach to Knowledge-based Word Sense Disambiguation,” inProceedings 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,” inProceedings of the International Conference on Control, Communication and Computing, pp. 638-641, 2015 [16] X. M. Jiang, L. R. Qiu,Y. Q. Li, “A Tibetan Word Sense Disambiguation Method based on Hownet and Chinese-Tibetan Parallel Corpora,” inProceedings of the International Standard Conference on Trustworthy Computing and Services, pp. 152-159, 2015 [17] J. Y. Duan, Y. Fu,X. Li, “Attribute Knowledge Mining for Chinese Word Sense Disambiguation,” inProceedings 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,” inProceedings of the 41st Latin American Computing Conference, pp. 53-60, 2015 |