From Sentences to DocumentsExtending Abstract Meaning Representation for Understanding Documents

  1. Paloma Moreda
  2. Armando Suárez
  3. Elena Lloret
  4. Estela Saquete
  5. Isabel Moreno
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2018

Issue: 60

Pages: 61-68

Type: Article

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Abstract

La sobreabundancia de información y su heterogeneidad requieren nuevas formas de acceder, procesar y generar conocimiento de acuerdo con las necesidades del usuario. Por ello, definir un formalismo adecuado para representar la información textual capaz de permitir a los ordenadores comprender y generar el lenguaje, es crucial para lograr esta tarea. Abstract Meaning Representation (AMR) es una representación del conocimiento estándar que puede capturar la información codificada en una oración en varios niveles lingüísticos. Sin embargo, su alcance se limita a una sola oración, y no se beneficia de la información semántica adicional que podría ayudar a la generación de diferentes tipos de textos. En este artículo propondremos un método que amplía la información proporcionada por AMR y la utiliza para representar documentos completos. En base a nuestra propuesta, definiremos una representación de texto estándar única, invariable e independiente, llamada representación canónica. A partir de la cual, y mediante un proceso de transformación, obtendremos diferentes variantes de texto que serán apropiadas para las necesidades de los usuarios

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Funding information

Research partially supported by the Spanish Government (grants TIN2015-65100-R; TIN2015-65136-C02-2-R).

Funders

    • TIN2015-65100-R

Bibliographic References

  • Banarescu, L., C. Bonial, S. Cai, M. Georgescu, K. Griffitt, U. Hermjakob, K. Knight, P. Koehn, M. Palmer, and N. Schneider. 2013. Abstract meaning representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 178–186, Sofia, Bulgaria, August. Association for Computational Linguistics.
  • Clark, K. and C. D. Manning. 2015. Entitycentric coreference resolution with model stacking. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1405–1415, Beijing, China, July. Association for Computational Linguistics.
  • Damonte, M., S. B. Cohen, and G. Satta. 2017. An incremental parser for abstract meaning representation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 536–546, Valencia, Spain, April. Association for Computational Linguistics.
  • Dohare, S. and H. Karnick. 2017. Text summarization using abstract meaning representation. CoRR, abs/1706.01678.
  • Flanigan, J., C. Dyer, N. A. Smith, and J. Carbonell. 2016. Generation from abstract meaning representation using tree transducers. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 731–739, San Diego, California, June. Association for Computational Linguistics.
  • Flanigan, J., S. Thomson, J. Carbonell, C. Dyer, and N. A. Smith. 2014. A discriminative graph-based parser for the abstract meaning representation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1426– 1436, Baltimore, Maryland, June. Association for Computational Linguistics.
  • Goodman, J., A. Vlachos, and J. Naradowsky. 2016. Noise reduction and targeted exploration in imitation learning for abstract meaning representation parsing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1–11, Berlin, Germany, August. Association for Computational Linguistics.
  • Langkilde, I. and K. Knight. 1998. Generation that exploits corpus-based statistical knowledge. In COLING-ACL ’98, Proceedings of the Conference, pages 704 –710, Montreal, Canada, August. Association for Computational Linguistics.
  • Liu, F., J. Flanigan, S. Thomson, N. Sadeh, and N. A. Smith. 2015. Toward abstractive summarization using semantic representations. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1077–1086, Denver, Colorado, May–June. Association for Computational Linguistics.
  • Llorens, H., E. Saquete, and B. NavarroColorado. 2013. Applying Semantic Knowledge to the Automatic Processing of Temporal Expressions and Events in Natural Language. Information Processing & Management, 49(1):179–197.
  • Martínez-Barco, P., A. F. Rodríguez, D. Tomás, E. Lloret, E. Saquete, F. Llopis, J. Peral, M. Palomar, J. M. G. Soriano, and M. T. Romà-Ferri. 2013. LEGOLANG: técnicas de deconstrucción aplicadas a las tecnologías del lenguaje humano. Procesamiento del Lenguaje Natural, 51:219–222.
  • Moro, A., F. Cecconi, and R. Navigli. 2014. Multilingual word sense disambiguation and entity linking for everybody. In Proceedings of the 2014 International Conference on Posters & Demonstrations Track Volume 1272, ISWC-PD’14, pages 25–28, Aachen, Germany, Germany. CEUR-WS.org.
  • Navarro-Colorado, B. and E. Saquete. 2016. Cross-document event ordering through temporal, lexical and distributional knowledge. Knowl.-Based Syst., 110:244–254.
  • Pourdamghani, N., K. Knight, and U. Hermjakob. 2016. Generating english from abstract meaning representations. In Proceedings of the 9th International Natural Language Generation conference, pages 21–25, Edinburgh, UK, September 5-8. Association for Computational Linguistics.
  • Saphra, N. and A. Lopez. 2015. Amrica: an amr inspector for cross-language alignments. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 36–40, Denver, Colorado, June. Association for Computational Linguistics.
  • Schuler, K. K. 2006. VerbNet: A BroadCoverage, Comprehensive Verb Lexicon. Ph.D. thesis, University of Pennsylvania.
  • UzZaman, N., H. Llorens, L. Derczynski, J. Allen, M. Verhagen, and J. Pustejovsky. 2013. Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations. In Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 1–9, Atlanta, Georgia, USA. ACL. ISBN: 978-1-937284-49-7.
  • Vanderwende, L., A. Menezes, and C. Quirk. 2015. An amr parser for english, french, german, spanish and japanese and a new amr-annotated corpus. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 26–30, Denver, Colorado, June. Association for Computational Linguistics.
  • Zhou, J., F. Xu, H. Uszkoreit, W. QU, R. Li, and Y. Gu. 2016. Amr parsing with an incremental joint model. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 680–689, Austin, Texas, November. Association for Computational Linguistics.
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