\(\hbox {NE}^2\): named event extraction engine

  • Swati Gupta
  • D. Patel
Regular Paper


Named event discovery using news headlines is an important problem with various applications in story telling, news event exploration, social media information fusion, etc. Named events are short phrases that represent the name of events like 2016 Rio Olympic Games, 2G Case, and Adarsh Society Scam. Existing work has largely focused on discovering events of named events using data mining and text mining techniques. However, the problem of discovering named event has not been addressed yet. In this paper, we present a system \(\hbox {NE}^{2}\) that uses pattern- based method to discover named events using news headlines. Along with named event, we also discover its categories, popular durations, popularity, and type of named events. Named events are categorized into candidate-level and high-level categories using URL information, and popular durations of named events are extracted using temporal information of news headlines. Our system generates 75,689 number of named events by analyzing 6.5 million news headlines. Out of 75,689 named events, 62,950 (82%) are categorized and popular duration are extracted for 73,288 (96.8%) number of named events. Based on performed experiments, our proposed system \(\hbox {NE}^{2}\) has 68% of accuracy for named events, 71.6% for named event’s category, and 78.4% for named event’s popular duration.


Named events Categories Popular durations 



The authors are grateful to Dr. Biplab Banerjee, Dr. Shitala, and Jayendra Barua for their support and suggestions. We thank to all the participants in the user studies.


  1. 1.
    Steinberger R, Pouliquen B, der Goot EV (2013) An introduction to the Europe Media Monitor family of applications. arXiv:1309.5290
  2. 2.
    Mazumder S, Bishnoi B, Patel D (2014) News headlines: what they can tell us? I-CARE 2014. ACM, New YorkCrossRefGoogle Scholar
  3. 3.
    Keneshloo Y, Cadena J, Korkmaz G, Ramakrishnan N (2014) Detecting and forecasting domestic political crises: a graph-based approach. In: WebSci’14. ACM, New York, Ny, USAGoogle Scholar
  4. 4.
    Kuzey E, Weikum G (2014) EVIN: building a knowledge base of events. In: WWW’14 companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, SwitzerlandGoogle Scholar
  5. 5.
    Li C, Sun A, Datta A (2012) Twevent: segment-based event detection from tweets. In: CIKM’12. ACM, New York, NY, USAGoogle Scholar
  6. 6.
    Alfonseca E, Pighin D, Garrido G (2013) HEADY: news headline abstraction through event pattern clustering. In proceedings of Association for Computer Linguistics (ACL- 2013), pp 1243–1253Google Scholar
  7. 7.
    Feng X, Huang L, Tang D, Ji H, Qin B, Liu T (2016) A language-independent neural network for event detection. In: Proceedings of ACL 2016, pp 66–71Google Scholar
  8. 8.
    Gupta K, Mittal V, Bishnoi B, Maheshwari S, Patel D (2016) AcT: accuracy-aware crawling techniques for cloud-crawler, vol 19. Kluwer, HinghamGoogle Scholar
  9. 9.
    Jain N, Gupta S, Patel D (2016) E3: keyphrase based news event exploration engine. In: HT’16. ACM, New York, NY, USAGoogle Scholar
  10. 10.
    Rusu D, Hodson J, Kimball A (2014) Unsupervised techniques for extracting and clustering complex events in news. Association for Computational Linguistics, BaltimoreCrossRefGoogle Scholar
  11. 11.
    Strötgen J, Gertz M (2010) HeidelTime: high quality rule-based extraction and normalization of temporal expressions. In: SemEval’10. Association for Computational Linguistics, Stroudsburg, PA, USAGoogle Scholar
  12. 12.
    Ghoreishi SN, Sun A (2013) Predicting event-relatedness of popular queries. In: CIKM’13. ACM, New York, NY, USAGoogle Scholar
  13. 13.
    Drakengren T, Jonsson P (1997) Towards a complete classification of tractability in Allen’s algebra. In: IJCAI’97. Morgan Kaufmann Publishers Inc., San Francisco, CA, USAGoogle Scholar
  14. 14.
    Liu Z, Li P, Zheng Y, Sun M (2009) Clustering to find exemplar terms for keyphrase extraction. In: EMNLP’09. Association for Computational Linguistics, Stroudsburg, PA, USAGoogle Scholar
  15. 15.
    Mihalcea R, Tarau P (2004) TextRank: bringing order into texts. Association for Computational Linguistics, BarcelonaGoogle Scholar
  16. 16.
    El-Kishky A, Song Y, Wang C, Voss CR, Han J (2014) Scalable topical phrase mining from text corpora. arXiv:1406.6312
  17. 17.
    Witten IH, Paynter GW, Frank E, Gutwin C, Nevill-Manning CG (1999) KEA: practical automatic keyphrase extraction. In: DL’99. ACM, New York, NY, USAGoogle Scholar
  18. 18.
    Naughton M, Naughton M, Kushmerick N, Carthy J (2006) Event extraction from heterogeneous news sources. In Proceedings of the AAAI Workshop on Event Extraction and Synthesis, pp 1–6Google Scholar
  19. 19.
    Nguyen T, Phung D, Adams B, Venkatesh S (2013) Event extraction using behaviors of sentiment signals and burst structure in social media. Knowl Inf Syst 37:279–304CrossRefGoogle Scholar
  20. 20.
    Kanhabua N, Ngoc Nguyen T, Nejdl W (2015) Learning to detect event-related queries for web search. In: WWW’15 companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, SwitzerlandGoogle Scholar
  21. 21.
    Strötgen J, Gertz M (2012) Event-centric search and exploration in document collections. In: JCDL’12. ACM, New York, NY, USAGoogle Scholar
  22. 22.
    Foley J, Bendersky M, Josifovski V (2015) Learning to extract local events from the web. In: SIGIR’15. ACM, New York, NY, USAGoogle Scholar
  23. 23.
    Radinsky K, Davidovich S, Markovitch S (2011) Learning causality for news events prediction. In Proceedings of WWWGoogle Scholar
  24. 24.
    Ritter A, Mausam, Etzioni O, Clark S (2012) Open domain event extraction from twitter. In: KDD’12. ACM, New York, NY, USAGoogle Scholar
  25. 25.
    Kuzey E, Vreeken J, Weikum G (2014) A fresh look on knowledge bases: distilling named events from news. In: CIKM’14. ACM, New York, NY, USAGoogle Scholar
  26. 26.
    Leban G, Fortuna B, Brank J, Grobelnik M (2014) Event registry: learning about world events from news. In: WWW’14 companion. ACM, New York, NY, USAGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations