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Towards Computing Inferences from English News Headlines

  • Elizabeth Jasmi GeorgeEmail author
  • Radhika Mamidi
Conference paper
  • 12 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1215)

Abstract

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its appearance in a bigger font and sometimes in colour print. In this paper, we suggest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a recent headline. The generated inferences could be useful for assessing the impact of the news headline on readers, including children. The understandability of the current state of social affairs depends significantly on the assimilation of the headlines. As the inferences that are independent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactic structure of the headlines and to compute inferences out of them. Considering the headline as the entry point to a piece of news and a source rich information about the news, we explored a headline’s potential of giving an idea about the current state of affairs, leveraging the syntax structure.

Keywords

Computing inferences Presuppositions Conventional implicatures Pragmatics News discourse News headline 

Notes

Acknowledgements

We would like to thank Dr. Monojit Choudhury, Microsoft Research-Bangalore, for suggesting this topic of research as part of the Computational Socio-pragmatics course he taught at IIIT-H. We would also like to thank all the anonymous reviewers for carefully reading through a previous version of this document and for offering valuable suggestions for improvement.

References

  1. 1.
    Cianflone, A., Feng, Y., Kabbara, J., Cheung, J.C.K.: Let’s do it “again”: a first computational approach to detecting adverbial presupposition triggers. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers (2018). https://aclweb.org/anthology/P18-1256. Accessed 4 Sept 2019
  2. 2.
    Potts, C.: Into the conventional-implicature dimension (2006).  https://doi.org/10.1111/j.1747-9991.2007.00089.x
  3. 3.
    Dor, D.: On newspaper headlines as relevance optimizers. J. Pragmat. 35, 695–721 (2003).  https://doi.org/10.1016/s0378-2166(02)00134-0CrossRefGoogle Scholar
  4. 4.
    van Dijk, T.A.: News as Discourse (1990)Google Scholar
  5. 5.
    Fromkin, V., Rodman, R., Hyams, N.: An introduction to Language, 8th edn. Thomson/Wadsworth, Boston (2007)Google Scholar
  6. 6.
    Angeli, G. Premkumar, M.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the Association of Computational Linguistics (ACL) (2015).  https://doi.org/10.3115/v1/p15-1034
  7. 7.
    Gattani, A.: Automated natural language headline generation using discriminative machine learning models. (2007). Simon Fraser University Homepage. https://summit.sfu.ca/item/2546. Accessed 2 Sept 2019
  8. 8.
    Straumann, H.: Newspaper Headlines: A Study in Linguistic Method. G. Allen & Unwin, Limited, London (1935). https://trove.nla.gov.au. Accessed 2 Sept 2019
  9. 9.
    Paul Grice, H.: Logic and conversation. In: Cole, P., Morgan, J.L. (eds.) Speech Acts, pp. 41–58. Academic Press, New York (1975).  https://doi.org/10.1057/9780230005853_5CrossRefGoogle Scholar
  10. 10.
    Iarovici, E., Amel, R.: The strategy of the headline. Semiotica 77–4, 441–459 (1989).  https://doi.org/10.1515/semi.1989.77.4.441CrossRefGoogle Scholar
  11. 11.
    Kronrod, A., Engel, O.: Accessibility theory and referring expressions in newspaper headlines. J. Pragmat. 33, 683–699 (2001).  https://doi.org/10.1016/s0378-2166(00)00013-8CrossRefGoogle Scholar
  12. 12.
    Levinson, S.C.: Pragmatics. Cambridge University Press, Cambridge (1983).  https://doi.org/10.1017/CBO9780511813313CrossRefGoogle Scholar
  13. 13.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014).  https://doi.org/10.3115/v1/p14-5010
  14. 14.
    Marneffe, M.-C., Manning, C.D.: Stanford typed dependencies manual (2008). Revised for the Stanford Parser v. 3.7.0 (2016). Stanford NLP group homepage https://nlp.stanford.edu/. Accessed 2 Sept 2019
  15. 15.
    Pilkington, A.: Poetic Effects: A Relevance Theory Perspective. John Benjamins, Amsterdam (2000).  https://doi.org/10.1075/pbns.75CrossRefGoogle Scholar
  16. 16.
    Rusu, D., Dali, L., Fortuna, B., Grobelnik, M., Mladenic, D.: Triplet extraction from sentences. In: Proceedings of the 10th International Multi-Conference Information Society-IS, pp. 8–12 (2007). SemanticScholar homepage https://pdfs.semanticscholar.org/
  17. 17.
    Yule, G.: Pragmatics. Oxford University Press, Oxford (1996).  https://doi.org/10.1017/CBO9780511757754.011CrossRefGoogle Scholar
  18. 18.
    Dagan, I., Glickman, O., Magnini, B.: The PASCAL recognising textual entailment challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006).  https://doi.org/10.1007/11736790_9CrossRefGoogle Scholar
  19. 19.
    Burger, J., Ferro, L.: Generating an entailment corpus from news headlines. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, Michigan, pp. 49–54. Association for Computational Linguistics, June 2005.  https://doi.org/10.3115/1631862.1631871
  20. 20.
    Spencer Kelly and many contributors: Compromise-modest natural-language processing in JavaScript. https://www.npmjs.com/package/compromise. Accessed 2 Sept 2019
  21. 21.
    Reuters Homepage. https://in.reuters.com/. Accessed 2 Sept 2019
  22. 22.
    The Hindu Homepage. https://www.thehindu.com/. Accessed 2 Sept 2019
  23. 23.
    BBC Homepage. https://www.bbc.com/. Accessed 2 Sept 2019
  24. 24.
    Pekar, V.: Discovery of event entailment knowledge from text corpora. Comput. Speech Lang. 22, 1–16 (2008)CrossRefGoogle Scholar
  25. 25.
    Weisman, H., Berant, J., Szpektor, I., Dagan, I.: Learning verb inference rules from linguistically-motivated evidence. In: EMNLP-CoNLL (2012)Google Scholar
  26. 26.
    de Marneffe, M.-C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. LREC (2006)Google Scholar
  27. 27.
    de Marneffe, M.-C., Nivre, J.: Dependency grammar. Annu. Rev. Linguist. 5, 197–218 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.LTRCInternational Institute of Information Technology, HyderabadHyderabadIndia

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