Have a personal or library account? Click to login
All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection Cover

All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

Open Access
|Dec 2020

References

  1. 1. Navigli, R. Word Sense Disambiguation: A Survey. – In: ACM Computing Surveys (CSUR), Vol. 41, 2009, No 2, 10.
  2. 2. Edmonds, P., S. Cotton. Senseval-2: Overview. – In: Proc. of 2nd International Workshop on Evaluating Word Sense Disambiguation Systems, Toulouse, France, 2001, pp. 1-6.
  3. 3. Snyder, B., M. Palmer. The English All-Words Task. – In: Proc. of 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SENSEVAL-3), Barcelona, Spain, 2004, pp. 41-43.
  4. 4. Pradhan, S., E. Loper, D. Dligach, M. Palmer. SemEval-2007 Task-17: English Lexical Sample, SRL and All Words. – In: Proc. of SemEval, 2007, pp. 87-92.
  5. 5. Navigli, R., D. Jurgens, D. Vannella. SemEval-2013 Task 12: Multilingual Word Sense Disambiguation. – In: Proc. of SemEval 2013, 2013, pp. 222-231.
  6. 6. Moro, A., R. Navigli. Semeval2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking. – In: Proc. of SemEval-2015, 2015, pp. 288-297.
  7. 7. Bolshina, A., N. Loukachevitch. Generating Training Data for Word Sense Disambiguation in Russian. – In: Proc. of Conference on Computational Linguistics and Intellectual Technologies Dialog-2020, 2020, pp. 119-132.10.28995/2075-7182-2020-19-119-132
  8. 8. Bolshina, A., N. Loukachevitch. Comparison of Genres in Word Sense Disambiguation Using Automatically Generated Text Collections. – In: Proc. of 4th International Conference Computational Linguistics in Bulgaria (CLIB’20), 2020, pp.155-164.10.2478/cait-2020-0049
  9. 9. Iacobacci, I., M. T. Pilehvar, R. Navigli. Embeddings for Word Sense Disambiguation: An Evaluation Study. – In: Proc. of ACL, Berlin, Germany, 2016, pp. 897-907.
  10. 10. Kagebäck, M., H. Salomonsson. Word Sense Disambiguation Using a Bidirectional LSTM. – In: Proc. of COLING, 2016, pp. 51-56.
  11. 11. Uslu, T., A. Mehler, D. Baumartz, A. Henlein, W. Hemati. FastSense: An Efficient Word Sense Disambiguation Classifier. – In: Proc. of LREC, 2018, pp. 1042-1046.
  12. 12. Raganato, A., C. D. Bovi, R. Navigli. Neural Sequence Learning Models for Word Sense Disambiguation. – In: Proc. of EMNLP, 2017, pp. 1156-1167.10.18653/v1/D17-1120
  13. 13. Vial, L., B. Lecouteux, D. Schwab. Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships. – arXiv preprint arXiv:1811.00960, 2018.
  14. 14. Yuan, D., J. Richardson, R. Doherty, C. Evans, E. Altendorf. Semi-Supervised Word Sense Disambiguation with Neural Models. – In: Proc. of COLING, 2016, pp. 1374-1385.
  15. 15. Le, M., M. Postma, J. Urbani, P. Vossen. A Deep Dive into Word Sense Disambiguation with LSTM. – In: Proc. of COLING, Association for Computational Linguistics, 2018, pp. 354-365.
  16. 16. Devlin, J., M.-W. Chang, K. Lee, K. Toutanova. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. – In: Proc. of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171-4186.
  17. 17. Peters, M., M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer. Deep Contextualized Word Representations. – In: Proc. of 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018, pp. 2227-2237.
  18. 18. Melamud, O., J. Goldberger, I. Dagan. Context2vec: Learning Generic Context Embedding with Bidirectional LSTM. – In: Proc. of COLING, 2016, pp. 51-61.
  19. 19. Kutuzov, A., E. Kuzmenko. To Lemmatize or Not to Lemmatize: How Word Normalisation Affects ELMo Performance in Word Sense Disambiguation. – In: Proc. of 1st NLPL Workshop on Deep Learning for Natural Language Processing, 2019, pp. 22-28.
  20. 20. Wiedemann, G., S. Remus, A. Chawla, C. Biemann. Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings. – arXiv preprint arXiv:1909.10430, 2019.
  21. 21. Du, J., F. Qi, M. Sun. Using BERT for Word Sense Disambiguation. – In: arXiv preprint arXiv:1909.08358, 2019.
  22. 22. Hadiwinoto, C., H. T. Ng, W. C. Gan. Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations. – In: arXiv preprint arXiv:1910.00194, 2019.
  23. 23. Bevilacqua, M., R. Navigli. Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation. – In: Proc. of International Conference on Recent Advances in Natural Language Processing (RANLP’19), 2019, pp. 122-131.10.26615/978-954-452-056-4_015
  24. 24. Levine, Y., B. Lenz, O. Dagan, D. Padnos, O. Sharir, S. Shalev-Shwartz, Y. Shoham. Sensebert: Driving Some Sense into BERT. – arXiv preprint arXiv:1908.05646, 2019.
  25. 25. Lesk, M. Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice Cream Cone. – In: Proc. of International Conference on Systems Documentation, 1986.
  26. 26. Luo, F., T. Liu, Q. Xia, B. Chang, Z. Sui. Incorporating Glosses into Neural Word Sense Disambiguation. – arXiv preprint arXiv:1805.08028, 2018.
  27. 27. Huang, L., C. Sun, X. Qiu, X. Huang. GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge. – arXiv preprint arXiv:1908.07245, 2019.
  28. 28. Blevins, T., L. Zettlemoyer. Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders. – arXiv preprint arXiv:2005.02590, 2020.
  29. 29. Loureiro, D., A. Jorge. Language Modelling Makes Sense: Propagating Representations through Wordnet for Full-Coverage Word Sense Disambiguation. – In: arXiv preprint arXiv:1906.10007, 2019.
  30. 30. Kumar, S., S. Jat, K. Saxena, P. Talukdar. Zero-Shot Word Sense Disambiguation Using Sense Definition Embeddings. – In: Proc. of 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 5670-5681.10.18653/v1/P19-1568
  31. 31. Bevilacqua, M., R. Navigli. Breaking through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information. – In: Proc. of 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 2854-2864.
  32. 32. Melacci, S., A. Globo, L. Rigutini. Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources. – In: Proc. of Eleventh International Conference on Language Resources and Evaluation (LREC’18), 2018.
  33. 33. Leacock, C., G. A. Miller, M. Chodorow. Using Corpus Statistics and WordNet Relations for Sense Identification. – Computational Linguistics, Vol. 24, 1998, No 1, pp. 147-165.
  34. 34. Miller, G. WordNet: A Lexical Database for English. – Communications of the ACM, Vol. 38, 1995, No 11, pp. 39-41.10.1145/219717.219748
  35. 35. Przybyła, P. How Big is Big Enough? Unsupervised Word Sense Disambiguation Using a Very Large Corpus. – arXiv preprint arXiv:1710.07960, 2017.
  36. 36. Mihalcea, R., D. I. Moldovan. An Iterative Approach to Word Sense Disambiguation. – In: FLAIRS Conference, 2000, pp. 219-223.
  37. 37. Seo, H. C., H. Chung, H. C. Rim, S. H. Myaeng, S. H. Kim. Unsupervised Word Sense Disambiguation Using WordNet Relatives. – Computer Speech & Language SPEC. – ISS, Vol. 18, 2004, No 3, pp. 253-273.10.1016/j.csl.2004.05.004
  38. 38. Yuret, D. KU: Word Sense Disambiguation by Substitution. – In: Proc. of 4th International Workshop on Semantic Evaluations, Association for Computational Linguistics, 2007, pp. 207-213.
  39. 39. Mihalcea, R. Bootstrapping Large Sense Tagged Corpora. – In: Proc. of 3rd International Conference on Language Resources and Evaluation (LREC’02). Vol. 1999. Las Palmas, Canary Islands, Spain, 2002.
  40. 40. Loukachevitch, N., I. Chetviorkin. Determining the Most Frequent Senses Using Russian Linguistic Ontology RuThes. – In: Proc. of Workshop on Semantic Resources and Semantic Annotation for Natural Language Processing and the Digital Humanities at NODALIDA 2015, 2015, pp. 21-27.
  41. 41. Henrich, V., E. Hinrichs, T. Vodolazova. Webcage: A Web Harvested Corpus Annotated with GermaNet Senses. – In: Proc. of 13th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 2012, pp. 387-396.
  42. 42. Agirre, E., O. L. De Lacalle. Publicly Available Topic Signatures for All WordNet Nominal Senses. – In: Proc: of LREC, 2004.
  43. 43. Barba, E., L. Procopio, N. Campolungo, T. Pasini, R. Navigli. MuLaN: Multilingual Label PropagatioN for Word Sense Disambiguation. – In: Proc. of IJCAI, 2020.
  44. 44. Bovi, C. D., J. Camacho-Collados, A. Raganato, R. Navigli. Eurosense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text. – In: Proc. of 55th Annual Meeting of the Association for Computational Linguistics, Vol. 2, 2017, pp. 594-600.
  45. 45. Pasini, T., R. Navigli. Train-o-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data. – In: Proc. of 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp.78-88.
  46. 46. Scarlini, B., T. Pasini, R. Navigli. Just “OneSeC” for Producing Multilingual Sense-Annotated Data. – In: Proc. of 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 699-709.10.18653/v1/P19-1069
  47. 47. Loukachevitch, N. V., G. Lashevich, A. A. Gerasimova, V. V. Ivanov, B. V. Dobrov. Creating Russian WordNet by Conversion. – In: Proc. of Conference on Computational Linguistics and Intellectual Technologies Dialog-2016, 2016, pp. 405-415.
  48. 48. Panchenko, A., A. Lopukhina, D. Ustalov, K. Lopukhin, N. Arefyev, A. Leontyev, N. Loukachevitch. RUSSE’2018: A Shared Task on Word Sense Induction for the Russian Language. – In: Proc. of Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue”, Moscow, Russia. RSUH, 2018, pp. 547-564.
  49. 49. Shavrina, T., O. Shapovalova. To the Methodology of Corpus Construction for Machine Learning: “Taiga” Syntax Tree Corpus and Parser. – In: Proc. of “CORPORA2017”, International Conference, Saint-Petersburg, 2017.
  50. 50. Kutuzov, A., E. Kuzmenko. WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models. – In: D. Ignatov et al., Eds. Analysis of Images, Social Networks and Texts, AIST 2016. Communications in Computer and Information Science. Vol. 661. Cham, Springer, 2017.
  51. 51. Pilehvar, M. T, J. Camacho-Collados. WiC: The Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations. – In: Proc. of NAACL-HLT, 2019, pp. 1267-1273.
DOI: https://doi.org/10.2478/cait-2020-0049 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 90 - 107
Submitted on: Oct 15, 2020
Accepted on: Oct 29, 2020
Published on: Dec 10, 2020
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Bolshina Angelina, Natalia Loukachevitch, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.