Skip to main content
SpringerLink
Log in

Giveme5W: Main Event Retrieval from News Articles by Extraction of the Five Journalistic W Questions

  • Conference paper
  • First Online:
Transforming Digital Worlds (iConference 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10766))

Included in the following conference series:

Abstract

Extraction of event descriptors from news articles is a commonly required task for various tasks, such as clustering related articles, summarization, and news aggregation. Due to the lack of generally usable and publicly available methods optimized for news, many researchers must redundantly implement such methods for their project. Answers to the five journalistic W questions (5Ws) describe the main event of a news article, i.e., who did what, when, where, and why. The main contribution of this paper is Giveme5W, the first open-source, syntax-based 5W extraction system for news articles. The system retrieves an article’s main event by extracting phrases that answer the journalistic 5Ws. In an evaluation with three assessors and 60 articles, we find that the extraction precision of 5W phrases is \( p = 0.7 \).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We use the POS-tag abbreviations from the Penn Treebank Project [33].

References

  1. Agence France-Presse: Taliban attacks German consulate in Northern Afghan city of Mazar-i-Sharif with truck bomb. The Telegraph (2016)

    Google Scholar 

  2. Allan, J., et al.: 1998 Topic detection and tracking pilot study: final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, pp. 194–218 (1998)

    Google Scholar 

  3. Altenberg, B.: Causal linking in spoken and written English. Studia Linguistica 38(1), 20–69 (1984)

    Article  Google Scholar 

  4. Asghar, N.: Automatic extraction of causal relations from natural language texts: a comprehensive survey. arXiv preprint arXiv:1605.07895 (2016)

  5. Best, C., et al.: Europe media monitor (2005)

    Google Scholar 

  6. Bethard, S., Martin, J.H.: Learning semantic links from a corpus of parallel temporal and causal relations. In: Proceedings of the 46th Annual Meeting of the ACL on Human Language Technologies, pp. 177–180 (2008)

    Google Scholar 

  7. Bird, S., et al.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., Sebastopol (2009)

    MATH  Google Scholar 

  8. Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language, pp. 152–164 (2005)

    Google Scholar 

  9. Charniak, E., Johnson, M.: Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In: Proceedings of the 43rd Annual Meeting on ACL, pp. 173–180 (2005)

    Google Scholar 

  10. Christian, D., et al.: The Associated Press Stylebook and Briefing on Media Law. Associated Press, New York (2014)

    Google Scholar 

  11. Das, A., Bandyaopadhyay, S., Gambäck, B.: The 5W structure for sentiment summarization-visualization-tracking. In: Gelbukh, A. (ed.) CICLing 2012. LNCS, vol. 7181, pp. 540–555. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28604-9_44

    Chapter  Google Scholar 

  12. Finkel, J.R., et al.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on ACL, pp. 363–370 (2005)

    Google Scholar 

  13. Girju, R.: Automatic detection of causal relations for question answering. In: Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering, vol. 12, pp. 76–83 (2003)

    Google Scholar 

  14. Greene, D., Cunningham, P.: Practical solutions to the problem of diagonal dominance in kernel document clustering. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 377–384 (2006)

    Google Scholar 

  15. Hamborg, F., et al.: Identification and analysis of media bias in news articles. In: Proceedings of the 15th International Symposium of Information Science (2017)

    Google Scholar 

  16. Hamborg, F., et al.: Matrix-based news aggregation: exploring different news perspectives. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, p. 10 (2017)

    Google Scholar 

  17. Hamborg, F., et al.: news-please: A generic news crawler and extractor. In: Proceedings of the 15th International Symposium of Information Science, pp. 218–223 (2017)

    Google Scholar 

  18. Hripcsak, G., Rothschild, A.S.: Agreement, the F-measure, and reliability in information retrieval. J. Am. Med. Inform. Assoc. 12(3), 296–298 (2005)

    Article  Google Scholar 

  19. Jurafsky, D.: Speech and Language Processing. Pearson Education India, New Delhi (2000)

    Google Scholar 

  20. Kekäläinen, J., Järvelin, K.: Using graded relevance assessments in IR evaluation. J. Am. Soc. Inform. Sci. Technol. 53(13), 1120–1129 (2002)

    Article  Google Scholar 

  21. Khoo, C.S.G., et al.: Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing. Lit. Linguist. Comput. 13(4), 177–186 (1998)

    Article  Google Scholar 

  22. Khoo, C.S.G.: Automatic identification of causal relations in text and their use for improving precision in information retrieval (1995)

    Google Scholar 

  23. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)

    Article  MATH  Google Scholar 

  24. Manning, C.D., et al.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  25. McKeown, K.R., et al.: Tracking and summarizing news on a daily basis with Columbia’s Newsblaster. In: Proceedings of the 2nd International Conference on Human Language Technology Research, pp. 280–285 (2002)

    Google Scholar 

  26. Oliver, P.E., Maney, G.M.: Political processes and local newspaper coverage of protest events: from selection bias to triadic interactions. Am. J. Sociol. 106(2), 463–505 (2000)

    Article  Google Scholar 

  27. Park, S., et al. NewsCube: delivering multiple aspects of news to mitigate media bias. In: Proceedings of SIGCHI 2009 Conference on Human Factors in Computing Systems, pp. 443–453 (2009)

    Google Scholar 

  28. parsedatetime - Parse human-readable date/time strings. https://github.com/bear/parsedatetime. Accessed 21 Aug 2017

  29. Parton, K., et al.: Who, what, when, where, why?: comparing multiple approaches to the cross-lingual 5W task. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 1, pp. 423–431 (2009)

    Google Scholar 

  30. Sharma, S., et al.: News event extraction using 5W1H approach & its analysis. Int. J. Sci. Eng. Res. – IJSER 4(5), 2064–2067 (2013)

    Google Scholar 

  31. Stemler, S.: An overview of content analysis. Pract. Assess. Res. Eval. 7(17), 137–146 (2001)

    Google Scholar 

  32. Tanev, H., Piskorski, J., Atkinson, M.: Real-time news event extraction for global crisis monitoring. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 207–218. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69858-6_21

    Chapter  Google Scholar 

  33. Taylor, A., et al.: The Penn treebank: an overview. In: Abeillé, A. (ed.) Treebanks. TLTB, vol. 20, pp. 5–22. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-010-0201-1_1

    Chapter  Google Scholar 

  34. Wang, W., et al.: Chinese news event 5W1H elements extraction using semantic role labeling. In: 2010 Third International Symposium on Information Processing (ISIP), pp. 484–489 (2010)

    Google Scholar 

  35. Yaman, S., et al.: Classification-based strategies for combining multiple 5-W question answering systems. In: INTERSPEECH, pp. 2703–2706 (2009)

    Google Scholar 

  36. Yaman, S., et al.: Combining semantic and syntactic information sources for 5-W question answering. In: INTERSPEECH, pp. 2707–2710 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Konstanz, Konstanz, Germany

    Felix Hamborg, Soeren Lachnit, Moritz Schubotz, Thomas Hepp & Bela Gipp

Authors
  1. Felix Hamborg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soeren Lachnit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moritz Schubotz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Hepp
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bela Gipp
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Felix Hamborg .

Editor information

Editors and Affiliations

  1. Northumbria University, Newcastle upon Tyne, United Kingdom

    Gobinda Chowdhury

  2. Northumbria University, Newcastle upon Tyne, United Kingdom

    Julie McLeod

  3. University of Sheffield, Sheffield, United Kingdom

    Val Gillet

  4. University of Sheffield, Sheffield, United Kingdom

    Peter Willett

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hamborg, F., Lachnit, S., Schubotz, M., Hepp, T., Gipp, B. (2018). Giveme5W: Main Event Retrieval from News Articles by Extraction of the Five Journalistic W Questions. In: Chowdhury, G., McLeod, J., Gillet, V., Willett, P. (eds) Transforming Digital Worlds. iConference 2018. Lecture Notes in Computer Science(), vol 10766. Springer, Cham. https://doi.org/10.1007/978-3-319-78105-1_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78105-1_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78104-4

  • Online ISBN: 978-3-319-78105-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation