Open Access
SHS Web Conf.
Volume 78, 2020
7e Congrès Mondial de Linguistique Française
Article Number 11006
Number of page(s) 15
Section Ressources et outils pour l'analyse linguistique
Published online 04 September 2020
  1. Arya, D. J., Hiebert, E. H., and Pearson, P. D. (2011). The effects of syntactic and lexical complexity on the comprehension of elementary science texts. Int Electronic Journal of Elementary Education, 4(1):107–125. [Google Scholar]
  2. Bautista, S., Gerva s, P., and Madrid, R. I. (2009). Feasibility analysis for semi-automatic conversion of text to improve readability. In Int Conf on Inform and Comm Technology and Accessibility (ICTA), pages 33–40. [Google Scholar]
  3. Beigman Klebanov, B., Knight, K., and Marcu, D. (2004). Text simplification for informationseeking applications. In Meersman, R. and Tari, Z., editors, On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE. Springer, LNCS vol 3290, Berlin, Heidelberg. [Google Scholar]
  4. Blake, C., Kampov, J., Orphanides, A., West, D., and Lown, C. (2007). Query expansion, lexical simplification, and sentence selection strategies for multi-document summarization. In DUC. [Google Scholar]
  5. Brouwers, L., Bernhard, D., Ligozat, A.-L., and Franc pis, T. (2014). Syntactic sentence simplification for French. In PITR workshop, pages 47–56. [Google Scholar]
  6. Brunato, D., Dell’Orletta, F., Venturi, G., and Montemagni, S. (2014). Defining an annotation scheme with a view to automatic text simplification. In CLICIT, pages 87–92. [Google Scholar]
  7. Carroll, J., Minnen, G., Canning, Y., Devlin, S., and Tait, J. (1998). Practical simplification of English newspaper text to assist aphasic readers. In AAAI98 Workshop on Integrating Artificial Intelligence and Assistive Technology, pages 7–10. [Google Scholar]
  8. Chandrasekar, R. and Srinivas, B. (1997). Automatic induction of rules for text simplification. Knowledge Based Systems, 10(3):183–190. [CrossRef] [Google Scholar]
  9. Chen, P., Rochford, J., Kennedy, D. N., Djamasbi, S., Fay, P., and Scott, W. (2016). Automatic text simplification for people with intellectual disabilities. In AIST, pages 1–9. [Google Scholar]
  10. De Belder, J. and Moens, M.-F. (2010). Text simplification for children. In Workshop on Accessible Search Systems of SIGIR, pages 1–8. [Google Scholar]
  11. Devlin, S. and Tait, J. (1998). The use of psycholinguistic database in the simplification of text for aphasic readers. In Linguistic Database, pages 161–173. [Google Scholar]
  12. Drndarevic, B., Stajner, S., and Saggion, H. (2012). Reporting simply: A lexical simplification strategy for enhancing text accessibility. In Easy to read on the web, pages 1–6. [Google Scholar]
  13. Germann, U. (2008). Yawat: Yet another word alignment tool. In ACL, editor, ACL-08: HLT Demo Session, pages 20–23, Columbus, USA. [CrossRef] [Google Scholar]
  14. Glavas, G. and Stajner, S. (2015). Simplifying lexical simplification: Do we need simplified corpora? In ACL-COLING, pages 63–68. [Google Scholar]
  15. Kim, Y.-S., Hullman, J., Burgess, M., and Adar, E. (2016). SimpleScience: Lexical simplification of scientific terminology. In EMNLP, pages 1–6. [Google Scholar]
  16. Laurent, D., Negre, S., and Ségue la, P. (2009). L’analyseur syntaxique Cordial dans Passage. In Traitement Automatique des Langues Naturelles (TALN). [Google Scholar]
  17. Leroy, G., Kauchak, D., and Mouradi, O. (2013). A user-study measuring the effects of lexical simplification and coherence enhancement on perceived and actual text difficulty. Int J Med Inform, 82(8):717–730. [CrossRef] [Google Scholar]
  18. Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. In Workshop at ICLR. [Google Scholar]
  19. Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. (1993). Introduction to wordnet: An on-line lexical database. Technical report, WordNet. [Google Scholar]
  20. Nisioi, S., Stajner, S., Ponzetto, S. P., and Dinu, L. P. (2017). Exploring neural text simplification models. In Ann Meeting of the Assoc for Comp Linguistics, pages 85–91. [Google Scholar]
  21. Paetzold, G. H. and Specia, L. (2016). Benchmarking lexical simplification systems. In LREC, pages 3074–3080. [Google Scholar]
  22. Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In EMNLP 2014, pages 1532–1543. [Google Scholar]
  23. Sennrich, R., Haddow, B., and Birch, A. (2016 ). Improving neural machine translation models with monolingual data. In Proc of the Ann Meeting of the Assoc for Comp Linguistics, pages 8696, Berlin, Germany. [Google Scholar]
  24. Shardlow, M. (2014). A survey of automated text simplification. Int J Advanced Computer Science and Applications, 1:1–13. [Google Scholar]
  25. Son, J. Y., Smith, L. B., and Goldstone, R. L. (2008). Simplicity and generalization: Short- cutting abstraction in children’s object categorizations. Cognition, 108:626–638. [CrossRef] [Google Scholar]
  26. Stymne, S., Tiedemann, J., Hardmeier, C., and Nivre, J. (2013). Statistical machine translation with readability constraints. In NODALIDA, pages 1–12. [Google Scholar]
  27. Vickrey, D. and Koller, D. (2008). Sentence simplification for semantic role labeling. In Annual Meeting of the Association for Computational Linguistics-HLT, pages 344–352. [Google Scholar]
  28. Vila, M., Anto’nia Mart, M., and Rodr iguez, H. (2011). Paraphrase concept and typology. A linguistically based and computationally oriented approach. Procesamiento del Lenguaje Natural, 46: 83–90. [Google Scholar]
  29. Wang, T., Chen, P., Amaral, K., and Qiang, J. (2016a). An experimental study of LSTM encoder-decoder model for text simplification. In IJCAI, pages 1–7. [Google Scholar]
  30. Wang, T., Chen, P., Rochford, J., and Qiang, J. (2016b). Text simplification using neural machine translation. In Proc of the AAA! Conference on Artificial Intelligence (AAAI- 16), pages 4270–4271. [Google Scholar]
  31. Wei, C.-H., Leaman, R., and Lu, Z. (2014). SimConcept: A hybrid approach for simplifying composite named entities in biomedicine. In BCB ’ 14, pages 138–146. [CrossRef] [Google Scholar]
  32. Wubben, S., van den Bosch, A., and Krahmer, E. (2012). Sentence simplification by monolingual machine translation. In Annual Meeting of the Association for Computational Linguistics, pages 1015–1024. [Google Scholar]
  33. Xu, W., Napoles, C., Pavlick, E., Chen, Q., and Callison-Burch, C. (2016). Optimizing statistical machine translation for text simplification. Transactions of the Association for Computational Linguistics, 4:401–415. [CrossRef] [Google Scholar]
  34. Yu, Q., Max, A., and Yvon, F. (2012). Revisiting sentence alignment algorithms for alignment visualization and evaluation. In BUCC workshop, pages 1–7. [Google Scholar]
  35. Zhang, X. and Lapata, M. (2017). Sentence simplification with deep reinforcement learning. In ACL, editor, Proc of the Conf on Empirical Methods in Natural Language Processing, pages 584–594, Copenhagen, Denmark. [Google Scholar]
  36. Zhao, S., Wang, H., and Liu, T. (2010). Leveraging multiple MT engines for paraphrase generation. In COLING, pages 1326–1334. [Google Scholar]
  37. Zhu, Z., Bernhard, D., and Gurevych, I. (2010). A monolingual tree-based translation model for sentence simplification. In COLING 2010, pages 1353–1361. [Google Scholar]

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