Ir al contenido

Documat

Métodos de aprendizaje profundo para la extracción de nombres metafóricos de flores y plantas

  • Autores: Tharindu Ranasinghe, Ruslan Mitkov Árbol académico, Amal Haddad Haddad, Damith Premasiri
  • Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 71, 2023, págs. 261-271
  • Idioma: español
  • Títulos paralelos:
    • Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plants
  • Enlaces
  • Resumen
    • Multiple

      El dominio de la Botánica es rico en términos metafóricos. Estos términos tienen un papel importante en la descripción e identificación de flores y plantas. Sin embargo, la identificación de este tipo de términos en el discurso es una tarea difícil. Esto puede conducir a errores en los procesos de traducción y otras tareas lexicográficas. Este proceso es aún más difícil cuando se trata de traducción automática, tanto en el caso de las unidades monoléxicas, como en el caso de las unidades multiléxicas. Uno de los desafíos a los que se enfrentan las aplicaciones del Procesamiento del Lenguaje Natural y las tecnologías de Traducción Automática es la identificación de términos basados en metáfora a través de métodos de aprendizaje profundo. En este estudio, tenemos el objetivo de rellenar este vacío a través del uso de trece modelos populares basados en transformadores, además del ChatGPT. Asimismo, demostramos que los modelos discriminativos aportan mejores resultados que los modelos de GPT-3.5. El mejor resultado alcanzó una puntuación de 92,2349% F1 en las tareas de identificación de nombres metafóricos de flores y plantas.

    • English

      The domain of Botany is rich with metaphorical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challenging when it comes to machine translation, both in the cases of single-word terms and multi-word terms. One of the recent concerns of Natural Language Processing (NLP) applications and Machine Translation (MT) technologies is the automatic identification of metaphor-based words in discourse through Deep Learning (DL). In this study, we seek to fill this gap through the use of thirteen popular transformer based models, as well as ChatGPT, and we show that discriminative models perform better than GPT-3.5 model with our best performer reporting 92.2349% F1 score in metaphoric flower and plant names identification task.

  • Referencias bibliográficas
    • Beltagy, I., K. Lo, and A. Cohan. 2019. SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on...
    • Brickell, C. 2012. Encyclopedia of plants and flowers. Dorling Kindersley, Santa Fe, New Mexico, USA.
    • Clark, K., M.-T. Luong, Q. V. Le, and C. D. Manning. 2020. ELECTRA: Pretraining text encoders as discriminators rather than generators. In...
    • Coll-Florit, M. and S. Climent. 2019. A new methodology for conceptual metaphor detection and formulation in corpora: A case study on a mental...
    • Conneau, A., K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov. 2020. Unsupervised...
    • Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding....
    • Dweck, A. 2004. A review of edelweiss. Sofw Journal, 130(9):65–71.
    • Dębowiak, P. and J. Waniakowa. 2019. Semantic motivation of plant names as a part of their etymology. page 173–200, 08.
    • Goodman, J. S. 1963. Malayalam color categories. Anthropological Linguistics, pages 1–12.
    • Group, P. 2007. Mip: A method for identifying metaphorically used words in discourse. Metaphor and symbol, 22(1):1–39.
    • Gutierrez, E. D., E. Shutova, T. Marghetis, and B. Bergen. 2016. Literal and metaphorical senses in compositional distributional semantic...
    • Jang, H., S. Moon, Y. Jo, and C. Rose. 2015. Metaphor detection in discourse. In Proceedings of the 16th Annual Meeting of the Special Interest...
    • Lakoff, G. and M. Johnson. 2008. Metaphors we live by. University of Chicago press.
    • Leong, C. W., B. B. Klebanov, C. Hamill, E. Stemle, R. Ubale, and X. Chen. 2020. A report on the 2020 vua and toefl metaphor detection shared...
    • Mitkov, R., H. M. Le An Ha, T. Ranasinghe, and V. Sosoni. 2023. Automatic generation of multiple-choice test items from paragraphs using deep...
    • Mu, J., H. Yannakoudakis, and E. Shutova. 2019. Learning outside the box: Discourse-level features improve metaphor identification. In Proceedings...
    • Nacey, S., A. G. Dorst, T. Krennmayr, and W. G. Reijnierse. 2019. Metaphor identification in multiple languages: MIPVU around the world, volume...
    • Nissan, E. 2014. Multilingual lexis, semantics, and onomasiology. terminological database modelling, by using the cupros metarepresentation...
    • Orăsan, C., R. Evans, and R. Mitkov. 2018. Intelligent text processing to help readers with autism. Intelligent Natural Language Processing:...
    • Parsley, K. M. 2020. Plant awareness disparity: A case for renaming plant blindness. Plants, People, Planet, 2(6):598–601.
    • Peñas, J. and J. Lorite. 2019. Biología de la conservación de plantas en Sierra Nevada. Universidad de Granada.
    • Premasiri, D., A. H. Haddad, T. Ranasinghe, and R. Mitkov. 2022. Transformerbased detection of multiword expressions in flower and plant names....
    • Premasiri, D. and T. Ranasinghe. 2022. Bert (s) to detect multiword expressions. In Proceedinsg of the 4th International Conference on Computational and...
    • Premasiri, D., T. Ranasinghe, W. Zaghouani, and R. Mitkov. 2022. DTW at qur’an QA 2022: Utilising transfer learning with transformers for...
    • Radford, A., K. Narasimhan, T. Salimans, I. Sutskever, et al. 2018. Improving language understanding by generative pretraining.
    • Ranasinghe, T., D. Sarkar, M. Zampieri, and A. Ororbia. 2021. WLV-RIT at SemEval- 2021 task 5: A neural transformer framework for detecting...
    • Ranasinghe, T. and M. Zampieri. 2021. MUDES: Multilingual detection of offensive spans. In Proceedings of the 2021 Conference of the North...
    • Ranasinghe, T., M. Zampieri, and H. Hettiarachchi. 2019. BRUMS at HASOC 2019: Deep Learning Models for Multilingual Hate Speech and Offensive...
    • Rastall, P. 1996. Metaphor and the names of plants. English Today, 12(2):30–31.
    • Razali, M. S., A. A. Halin, Y.-W. Chow, N. M. Norowi, and S. Doraisamy. 2022. Deep and contextually engineered features for metaphor detection.
    • Rodríguez Penagos, C. et al. 2005. Metalinguistic information extraction from specialized texts to enrich computational lexicons. Ph.D. thesis,...
    • Ruiz de Mendoza Ibáñez, F. J. 2017. Conceptual complexes in cognitive modeling. Revista Española de Lingüística Aplicada/ Spanish Journal...
    • Štajner, S., V. Yaneva, R. Mitkov, and S. P. Ponzetto. 2017. Effects of lexical properties on viewing time per word in autistic and neurotypical...
    • Su, C., F. Fukumoto, X. Huang, J. Li, R. Wang, and Z. Chen. 2020. Deepmet: A reading comprehension paradigm for token-level metaphor detection....
    • Tjong Kim Sang, E. F. and F. De Meulder. 2003. Introduction to the CoNLL- 2003 shared task: Language-independent named entity recognition....
    • Turney, P., Y. Neuman, D. Assaf, and Y. Cohen. 2011. Literal and metaphorical sense identification through concrete and abstract context....
    • Urena Gomez-Moreno, J. M. and P. Faber. 2010. Strategies for the semi-automatic retrieval of metaphorical terms. Metaphor and Symbol, 26(1):23–52.
    • Uyangodage, L., T. Ranasinghe, and H. Hettiarachchi. 2021. Can multilingual transformers fight the COVID-19 infodemic? In Proceedings of the...
    • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances...
    • Vitez, A. Z., M. Brglez, M. Robnik-Šikonja, T. Škvorc, A. Vezovnik, and S. Pollak. 2022. Extracting and analysing metaphors in migration media...
    • Yaneva, V. 2016. Assessing text and web accessibility for people with autism spectrum disorder
Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno