Ir al contenido

Documat

Are rule-based approaches a thing of the past?: The case of anaphora resolution

  • Autores: Ruslan Mitkov Árbol académico, Le An Ha
  • Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 73, 2024, págs. 15-27
  • Idioma: inglés
  • Títulos paralelos:
    • ¿Son los métodos basados en reglas cosa del pasado? El caso de la resolución de la anáfora
  • Enlaces
  • Resumen
    • español

      En este artículo evaluamos y comparamos nuevas variantes de un conocido algoritmo de resolución de anáforas basado en reglas con la versión original. Buscamos establecer si los enfoques que se benefician de aprendizaje profundo, grandes modelos de lenguaje (LLMs) y datos de eye-tracking (siempre) superan al algoritmo original basado en reglas. Los resultados de este estudio sugieren que, aunque los algoritmos basados en aprendizaje profundo y grandes modelos de lenguaje suelen rendir mejor que los basados en reglas, no siempre es así. Por lo tanto, sostenemos que los enfoques basados en reglas siguen teniendo cabida en la investigación actual.

    • English

      In this paper, we evaluate and compare new variants of a popular rule-based anaphora resolution algorithm with the original version. We seek to establish whether configurations that benefit from Deep Learning, LLMs and eye-tracking data (always) outperform the original rule-based algorithm. The results of this study suggest that while algorithms based in Deep Learning and LLMs usually perform better than rule-based ones, this is not always the case, and we argue that rule-based approaches still have a place in today’s research.

  • Referencias bibliográficas
    • Baldwin, B. 1998. Coreference as the foundations for link analysis over free text databases. In Content Visualization and Intermedia Representations...
    • Barrett, M., J. Bingel, F. Keller, and A. Søgaard. 2016. Weakly supervised part-of-speech tagging using eye-tracking data. In Proceedings...
    • Charniak, E. 2000. A maximum-entropy-inspired parser. In 1st Meeting of the North American Chapter of the Association for Computational Linguistics.
    • Clark, K. and C. D. Manning. 2015. Entity-centric coreference resolution with model stacking. In C. Zong and M. Strube, editors, Proceedings...
    • Clark, K. and C. D. Manning. 2016. Deep reinforcement learning for mention-ranking coreference models. In J. Su, K. Duh, and X. Carreras,...
    • Colson, J.-P. 2019. Multi-word units in machine translation: why the tip of the iceberg remains problematic – and a tentative corpus-driven...
    • Cop, U., N. Dirix, D. Drieghe, and W. Duyck. 2016. Presenting GECO: An eyetracking corpus of monolingual and bilingual sentence reading. Behavior...
    • Cunningham, H., D. Maynard, K. Bontcheva, V. Tablan, and Y. Wilks. 2000. Experience using GATE for NLP R&D. In R. Zajac, editor, Proceedings...
    • Duchowski, A. T. 2017. Eye Tracking Methodology: Theory and Practice. Springer Publishing Company, Incorporated, 3rd edition.
    • Evans, R. 2001. Applying machine learning toward an automatic classification of it. Literary and linguistic computing, 16(1):45–58.
    • Foraker, S. and B. McElree. 2007. The role of prominence in pronoun resolution: Active versus passive representations. Journal of Memory and...
    • Ge, N., J. Hale, and E. Charniak. 1998. A statistical approach to anaphora resolution. In Sixth Workshop on Very Large Corpora.
    • Kennedy, A., R. Hill, and J. Pynte. 2003. The Dundee corpus. In Proceedings of the 12th European Conference on Eye Movement (ECEM-2003).
    • Lappin, S. and H. J. Leass. 1994. An algorithm for pronominal anaphora resolution. Computational Linguistics, 20(4):535–561.
    • Li, Y., P. Musilek, M. Reformat, and L. Wyard-Scott. 2009. Identification of pleonastic it using the web. J. Artif. Int. Res., 34(1):339–389,...
    • Liu, R., R. Mao, A. T. Luu, and E. Cambria. 2023. A brief survey on recent advances in coreference resolution. Artificial Intelligence Review.
    • Meng, Y. and A. Rumshisky. 2018. Triad-based neural network for coreference resolution. In E. M. Bender, L. Derczynski, and P. Isabelle, editors,...
    • Mitkov, R. 1996. Towards a more efficient use of PC-based MT in education. In Proceedings of Translating and the Computer 18, London, UK,...
    • Mitkov, R. 1998. Robust pronoun resolution with limited knowledge. In 36th Annual Meeting of the Association for Computational Linguistics...
    • Mitkov, R. 2002. Anaphora Resolution. Longman.
    • Mitkov, R., editor. 2003. The Oxford Handbook of Computational Linguistics. Oxford University Press.
    • Mitkov, R. 2006. Fully automatic anaphora resolution for English and Bulgarian. In S. K. M. Slavcheva, M. and G. Angelova, editors, Readings...
    • Mitkov, R. 2019. Computer vs. human intelligence. Keynote speech at the Refinitiv conference, City of London.
    • Mitkov, R., editor. 2022. The Oxford Handbook of Computational Linguistics. Oxford University Press, 2nd substantially revised edition.
    • Mitkov, R. and C. Barbu. 2000. Mutual enhancement of performance: bilingual pronoun resolution for English and French. In DAARRC2000 - Discourse,...
    • Mitkov, R., L. Belguith, and M. Stys. 1998. Multilingual robust anaphora resolution. In N. Ide and A. Voutilainen, editors, Proceedings of...
    • Mitkov, R. and L. H. Belguith. 1998. Robust pronoun resolution with limited knowledge: a high success rate approach for English and Arabic....
    • Mitkov, R., R. Evans, and C. Orasan. 2002. A new, fully automatic version of mitkov’s knowledge-poor pronoun resolution method. In A. Gelbukh,...
    • Mitkov, R. and M. Stys. 1997. Robust reference resolution with limited knowledge: high precision genre-specific approach for English and Polish....
    • Nito´n, B., P. Morawiecki, and M. Ogrodniczuk. 2018. Deep neural networks for coreference resolution for Polish. In N. Calzolari, K. Choukri,...
    • Orasan, C. and R. Evans. 2001. Learning to identify animate references. In Proceedings of the ACL 2001 Workshop on Computational Natural Language...
    • Peters, M., M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings...
    • Plu, J., R. Prokofyev, A. Tonon, P. Cudré-Mauroux, D. E. Difallah, R. Troncy, and G. Rizzo. 2018. Sanaphor++: Combining deep neural...
    • Rayner, K. 1998. Eye movements in reading and information processing: 20 years of research. Psychological bulletin, 124(3):372.
    • Rayner, K. and S. A. Duffy. 1986. Lexical complexity and fixation times in reading: Effects of word frequency, verb complexity, and lexical...
    • Rayner, K., A. Pollatsek, J. Ashby, and C. Clifton Jr. 2012. Psychology of reading.
    • Rohanian, O., S. Taslimipoor, V. Yaneva, and L. A. Ha. 2017. Using gaze data to predict multiword expressions. In Proceedings of the International...
    • Rotsztejn, J. 2018. Learning from cognitive features to support natural language processing tasks. Master’s thesis, ETH Zurich.
    • Stuckardt, R. 2002. Machine-learning-based vs. manually designed approaches to anaphor resolution: the best of two worlds. In Proc.4th Discourse...
    • Stuckardt, R. 2003. Coreference-based summarization and question answering: a case for high precision anaphor resolution. In Proc. 2003 Int....
    • Stuckardt, R. 2005. A machine learning approach to preference strategies for anaphor resolution. In Anaphora Processing Linguistic, Cognitive...
    • Tanev, H. and R. Mitkov. 2002. Shallow language processing architecture for Bulgarian. In COLING 2002: The 19th International Conference on...
    • Tapanainen, P. and T. Jarvinen. 1997. A non-projective dependency parser. In Fifth Conference on Applied Natural Language Processing, pages...
    • Trinh, T. H. and Q. V. Le. 2018. A simple method for commonsense reasoning. CoRR, abs/1806.02847.
    • Vadász, N. 2023. Resolving Hungarian anaphora with ChatGPT. In K. Ekˇstein, F. Pártl, and M. Konopík, editors, Text, Speech, and Dialogue,...
    • Wiseman, S., A. M. Rush, and S. M. Shieber. 2016. Learning global features for coreference resolution. In K. Knight, A. Nenkova, and O. Rambow,...
    • Yaneva, V., L. A. Ha, R. Evans, and R. Mitkov. 2018. Classifying referential and non-referential it using gaze. In E. Riloff, D. Chiang, J....
    • Yang, X., E. Peynetti, V. Meerman, and C. Tanner. 2022. What GPT knows about who is who. In S. Tafreshi, J. Sedoc, A. Rogers, A. Drozd, A....
Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno