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Issue No.06 - Nov.-Dec. (2012 vol.27)
pp: 26-35
Antonia Azzini , Università degli Studi di Milano
Célia da Costa Pereira , Université de Nice Sophia Antipolis
Mauro Dragoni , Fondazione Bruno Kessler
Andrea G.B. Tettamanzi , Università degli Studi di Milano
The proposed approach to word sense disambiguation uses an evolutionary algorithm to automatically design the structure and learn the connection weights of neural networks.
Evolutionary computation, Text mining, Artificial neural networks, Neural networks, Encoding, neural networks, Evolutionary computation, Text mining, Artificial neural networks, Neural networks, Encoding, evolutionary algorithms, word sense disambiguation
Antonia Azzini, Célia da Costa Pereira, Mauro Dragoni, Andrea G.B. Tettamanzi, "A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation", IEEE Intelligent Systems, vol.27, no. 6, pp. 26-35, Nov.-Dec. 2012, doi:10.1109/MIS.2011.108
1. Z. Harris, “Distributional Hypothesis,” Word, vol. 10, no. 23, 1954, pp. 146–162.
2. D.M. Escudero, G.L. Màrquez, and G. Rigau, “Supervised Corpus-Based Methods for WSD,” Word Sense Disambiguation: Algorithms and Applications Springer, 2006, pp. 167–207.
3. D. Mart ínez, O.L. de Lacalle, and E. Agirre, “On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation,” J. Artificial Intelligence Research vol. 33, no. 9, 2008, pp. 79–107.
4. P. Chen et al., “Word Sense Disambiguation with Automatically Acquired Knowledge,” IEEE Intelligent Systems vol. 27, no. 4, 2012, pp. 46–55.
5. R. Navigli and S. Ponzetto, “Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems,” Proc. 48th Ann. Meeting Assoc. for Com-putational Linguistics Assoc. for Computational Linguistics, 2010, pp. 1522–1531.
6. J. Veronis and N. Ide, “Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries,” Proc. 13th Conf. Computational Linguistics (COLING 90) vol. 2,Assoc. for Computational Linguistics, 1990, pp. 389–394.
7. R. Navigli, “Word Sense Disambiguation: A Survey,” ACM Computing Surveys vol. 41, no. 2, 2009, pp. 1–69.
8. E. Agirre and O.L. de Lacalle, “Publicly Available Topic Signatures for All WordNet Nominal Senses,” Proc. 4th Int'l Conf. Languages, Resources, and Evaluations (LREC 04), ELDA, 2004.
9. C. Leacock, M. Chodorow, and G. Miller, “Using Corpus Statistics and WordNet Relations for Sense Identification,” Computational Linguistics vol. 24, no. 1, 1998, pp. 147–165.
10. G. Hinton, J. Mc Clelland, and D. Rumelhart, “Distributed Representations,” Parallel Distributed Processing: Explorations in the Microstructure of Cognition MIT Press, 1986.
11. G. Cottrell, A Connectionist Approach to Word Sense Disambiguation Pitman, 1989.
12. D. Yarowsky and R. Florian, “Evaluating Sense Disambiguation across Diverse Parameter Spaces,” Natural Language Eng. vol. 8, no. 4, 2002, pp. 293–310.
13. A. Azzini and A. Tettamanzi, “Evolving Neural Networks for Static Single-Position Automated Trading,” J. Artificial Evolution and Applications Jan. 2008, pp. 1–17.
14. A. Azzini et al., “Evolving Neural Networks for Word Sense Disambiguation,” Proc. Int'l Conf. Hybrid Intelligent Systems (HIS 08), IEEE CS, 2008, pp. 332–337.
15. S. Pradhand et al., “SemEval 2007 Task 17: English Lexical Sample, SRL, and All Words,” Proc. 4th Int'l Workshop on Semantic Evaluations (SemEval 07), Assoc. for Computational Linguistics, 2007, pp. 87–92.
16. E. Hovy et al., “OntoNotes: The 90% Solution,” Proc. Human Language Technology Conf. ACM, 2006, pp. 57–60.
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