Paraphrase and Textual Entailment Generation
One particular information can be conveyed by many different sentences. This variety concerns the choice of vocabulary and style as well as the level of detail (from laconism or succinctness to total verbosity). Although verbosity in written texts is considered bad style, generated verbosity can help natural language processing (NLP) systems to fill in the implicit knowledge.
The paper presents a rule-based system for paraphrasing and textual entailment generation in Czech. The inner representation of the input text is transformed syntactically or lexically in order to produce two type of new sentences: paraphrases (sentences with similar meaning) and entailments (sentences that humans will infer from the input text). The transformations make use of several language resources as well as a natural language generation (NLG) subsystem.
The paraphrases and entailments are annotated by one or more annotators. So far, we annotated 3,321 paraphrases and textual entailments, from which 1,563 were judged correct (47.1 %), 1,238 (37.3 %) were judged incorrect entailments, and 520 (15.6 %) were judged non-sense.
Paraphrasing and textual entailment can be put into effect in chatbots, text summarization or question answering systems. The results can encourage application-driven creation of new language resources or improvement of the current ones.
Keywordstextual entailment paraphrase natural language generation
Unable to display preview. Download preview PDF.
- 2.Graesser, A.: Prose Comprehension Beyond the Word. Springer (1981)Google Scholar
- 3.Akhmatova, E.: Textual entailment resolution via atomic propositions. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (April 2005)Google Scholar
- 4.Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textual entailment methods. CoRR abs/0912.3747 (2009)Google Scholar
- 5.Clark, P., Fellbaum, C., Hobbs, J.R.: The boeing-princeton-ISI (BPI) textual entailment test suite (December 2006), http://www.cs.utexas.edu/~pclark/bpi-test-suite/ (accessed online April 14, 2014)
- 6.Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press (May 1998)Google Scholar
- 8.Zeller, B., Padó, S.: A search task dataset for German textual entailment. In: Proceedings of the 10th International Conference on Computational Semantics (IWCS), Potsdam (2013)Google Scholar
- 9.Dagan, I., Roth, D., Zanzotto, F.M.: Tutorial notes. In: 5th Annual Meeting of the Association of Computational Linguistics. The Association of Computational Linguistics, Prague (2007)Google Scholar
- 10.Šmerk, P.: Towards Computational Morphological Analysis of Czech. Dissertation, Masaryk University in Brno (2010)Google Scholar
- 11.Šmerk, P.: K morfologické desambiguaci češtiny (Towards morphological disambiguation of Czech). Thesis proposal, Masaryk University (2008)Google Scholar
- 12.Kovář, V., Horák, A., Jakubíček, M.: Syntactic analysis using finite patterns: A new parsing system for Czech. In: Human Language Technology. Challenges for Computer Science and Linguistics, Poznań, Poland, November 6-8, Revised Selected Papers, pp. 161–171 (2011)Google Scholar
- 13.Grác, M.: Rapid Development of Language Resources. Dissertation, Masaryk University in Brno (2013)Google Scholar
- 14.Pala, K., Smrž, P.: Building Czech WordNet. Romanian Journal of Information Science and Technology 2004(7), 79–88 (2004)Google Scholar
- 16.Hlaváčková, D., Horák, A.: VerbaLex – new comprehensive lexicon of verb valencies for Czech. In: Proceedings of the Slovko Conference (2005)Google Scholar
- 17.Grepl, M., Karlík, P.: Skladba spisovné češtiny. Edice Učebnice pro vysoké školy. Státní naklad (1986)Google Scholar