Skip to main content

Information Extraction: Past, Present and Future

  • Chapter
  • First Online:
Multi-source, Multilingual Information Extraction and Summarization

Abstract

In this chapter we present a brief overview of Information Extraction, which is an area of natural language processing that deals with finding factual information in free text. In formal terms, facts are structured objects, such as database records. Such a record may capture a real-world entity with its attributes mentioned in text, or a real-world event, occurrence, or state, with its arguments or actors: who did what to whom, where and when. Information is typically sought in a particular target setting, e.g., corporate mergers and acquisitions. Searching for specific, targeted factual information constitutes a large proportion of all searching activity on the part of information consumers. There has been a sustained interest in Information Extraction for over two decades, due to its conceptual simplicity on one hand, and to its potential utility on the other. Although the targeted nature of this task makes it more tractable than some of the more open-ended tasks in NLP, it is replete with challenges as the information landscape evolves, which also makes it an exciting research subject.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

Notes

  1. 1.

    Zero-anaphora are typical in many languages—including the Romance and Slavic languages, Japanese, etc.—in which subjects may not be explicitly realized.

  2. 2.

    http://www-nlpir.nist.gov/related_projects/muc/.

  3. 3.

    http://www.itl.nist.gov/iad/mig/tests/ace/.

  4. 4.

    http://projects.ldc.upenn.edu.

  5. 5.

    http://www.clips.ua.ac.be/conll2002/ner/.

  6. 6.

    http://www.nist.gov/tac/.

  7. 7.

    http://www.senseval.org.

  8. 8.

    http://www-nlpir.nist.gov/related_projects/muc/proceedings/muc_7_toc.htmlconference.

  9. 9.

    Industrial-strength solutions for NER for various languages exist.

  10. 10.

    Various formalisms are used to encode patterns, ranging from character-level regular expressions, through context-free grammars to unification-based formalisms.

  11. 11.

    Some examples of NLP tools which are relevant in the context of developing IE systems can be found at: http://alias-i.com/lingpipe/web/competition.html.

  12. 12.

    Standard references are available for application of supervised learning algorithms, e.g., [70], and in particular to NLP tasks, cf. [41].

  13. 13.

    This kind of labeling is actually more similar in nature to named entity classification, except that the information extracted is about not the nature of node in a semantic network—i.e., a (possibly named) entity—but the nature of an edge, i.e., a link between two nodes. The information extracted focuses mostly on what label the system should attach to the analyzed object.

  14. 14.

    For an example of learning for anaphora resolution, cf. [46].

  15. 15.

    Typically a substantial subset of the annotated data will need to be reserved for testing and evaluation purposes—otherwise we cannot estimate the quality of performance on unseen data. We cannot test on the same data on which the learning algorithm was trained.

  16. 16.

    In contrast to a “passive” learner, which learns from a set of annotated examples—negative as well as positive—provided by the teacher.

  17. 17.

    These bootstrapping-type methods are sometimes called “unsupervised”, but that is rather a misnomer; a more appropriate term would be weakly or minimally supervised learning.

  18. 18.

    http://www.facebook.com/.

  19. 19.

    http://twitter.com/.

  20. 20.

    Trained on extractions heuristically generated from PennTreebank.

References

  1. Andersen, P., Hayes, P., Huettner, A., Schmandt, L., Nirenburg, I., Weinstein, S.: Automatic extraction of facts from press releases to generate news stories. In: Proceedings of the 3rd Conference on Applied Natural Language Processing, ANLC ’92, Trento, pp. 170–177. Association for Computational Linguistics, Stroudsburg (1992)

    Google Scholar 

  2. Aone, C., Halverson, L., Hampton, T., Ramos-Santacruz, M., Hampton, T.: SRA: description of the IE2 system used for MUC-7 In: Proceedings of MUC-7. Morgan Kaufmann, Columbia (1999)

    Google Scholar 

  3. Aone, C., Ramos-Santacruz, M.: REES: a large-scale relation and event extraction system. In: Proceedings of the 6th Conference on Applied Natural Language Processing, ANLP 2000, Seattle, pp. 76–83. Association for Computational Linguistics, Stroudsburg (2000)

    Google Scholar 

  4. Appelt, D.: Introduction to information extraction. AI Commun.12, 161–172 (1999)

    Google Scholar 

  5. Artiles, J., Borthwick, A., Gonzalo, J., Sekine, S., Amigï£, E.: WePS-3 evaluation campaign: overview of the web people search clustering and attribute extraction tasks. In: Braschler, M., Harman, D., Pianta, E. (eds.) CLEF (Notebook Papers/LABs/Workshops), Padua (2010)

    Google Scholar 

  6. Banko, M., Cafarella, M., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the 20th International Joint Conference on Artifical intelligence, Hyderabad, pp. 2670–2676. Morgan Kaufmann, San Francisco (2007)

    Google Scholar 

  7. Banko, M., Etzioni, O.: The tradeoffs between open and traditional relation extraction. In: Proceedings of ACL-08: HLT, Columbus, pp. 28–36. Association for Computational Linguistics, Columbus (2008)

    Google Scholar 

  8. Benson, E., Haghighi, A., Barzilay, R.: Event discovery in social media feeds. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies – Vol. 1, pp. 389–398. Association for Computational Linguistics, Stroudsburg (2011)

    Google Scholar 

  9. Bikel, D., Miller, S., Schwartz, R., Weischedel, R.: Nymble: a high-performance learning name-finder. In: Proceedings of the 5th Applied Natural Language Processing Conference, Washington. Association for Computational Linguistics, Washington, DC (1997)

    Google Scholar 

  10. Califf, M.E., Greenwood, M.A., Stevenson, M., Yangarber, R. (eds.): In: Proceedings of the Workshop on Information Extraction Beyond The Document. COLING/ACL, Sydney (2006)

    Google Scholar 

  11. Charniak, E.: Statistical Language Learning. MIT, Cambridge (1993)

    Google Scholar 

  12. Chen, Z., Ji, H.: Can one language bootstrap the other: a case study on event extraction. In: Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing, SemiSupLearn ’09, Boulder, pp. 66–74. Association for Computational Linguistics, Stroudsburg (2009)

    Google Scholar 

  13. Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. University of Maryland, College Park (1999)

    Google Scholar 

  14. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: a framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, Philadelphia (2002)

    Google Scholar 

  15. Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R.: Automatic content extraction (ACE) program – task definitions and performance measures. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004) (2004)

    Google Scholar 

  16. Downey, D., Etzioni, O., Soderland, S.: A probabilistic model of redundancy in information extraction. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, IJCAI’05, Edinburgh, pp. 1034–1041. Morgan Kaufmann, San Francisco (2005)

    Google Scholar 

  17. Drożdżyński, W., Krieger, H.U., Piskorski, J., Schäfer, U., Xu, F.: Shallow processing with unification and typed feature structures – foundations and applications. Künstliche Intell. 1/04, 17–23 (2004)

    Google Scholar 

  18. Etzioni, O., Cafarella, M., Downey, D., Popescu, A.M., Shaked, T., Soderland, S., Weld, D., Alexander, A.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165, 91–134 (2005)

    Google Scholar 

  19. Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam: Open information extraction: the second generation. In: Proceedings of IJCAI 2011, Barcelona, pp. 3–10 (2011)

    Google Scholar 

  20. Fader, A., Soderland, S., Etzioni, O.: Extracting sequences from the web. In: Proceedings of the ACL 2010 Conference Short Papers, Uppsala, pp. 286–290. Association for Computational Linguistics, Uppsala (2010)

    Google Scholar 

  21. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL ’05, Michigan, pp. 363–370. Association for Computational Linguistics, Stroudsburg (2005)

    Google Scholar 

  22. Gao, J., Wu, A., Li, M., ning Huang, C.: Chinese word segmentation and named entity recognition: a pragmatic approach. Comput. Linguist. 31, 574 (2005)

    Google Scholar 

  23. Grishman, R., Sundheim, B.: Message understanding conference – 6: a brief history. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING), Kopenhagen, pp. 466–471. The Association for Computational Linguistics, Stroudsburg (1996)

    Google Scholar 

  24. Hobbs, J.R., Appelt, D., Bear, J., Israel, D., Kameyama, M., Stickel, M., Tyson, M.: FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In: Roche, E., Schabes, Y. (eds.) Finite State Language Processing. MIT, Cambridge (1997)

    Google Scholar 

  25. Humphreys, K., Gaizauskas, R., Huyck, C., Mitchell, B., Cunningham, H., Wilks, Y.: University of sheffield: description of the LaSIE-II system and used for MUC-7. In: Proceedings of MUC-7, Virginia. SAIC (1998)

    Google Scholar 

  26. Huttunen, S., Yangarber, R., Grishman, R.: Complexity of event structure in information extraction. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002). Taipei (2002)

    Google Scholar 

  27. Iida, R., Poesio, M.: A cross-lingual ILP solution to zero anaphora resolution. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, The Association for Computer Linguistics, Portland, Oregon, 19–24 June 2011, pp. 804–813 (2011)

    Google Scholar 

  28. Jacobs, P., Rau, L.: SCISOR: extracting information from on-line news. Commun. ACM 33, 88–97 (1990)

    Google Scholar 

  29. Ji, H.: Challenges from information extraction to information fusion. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ’10, pp. 507–515. Association for Computational Linguistics, Stroudsburg (2010)

    Google Scholar 

  30. Ji, H., Grishman, R.: Refining event extraction through cross-document inference. In: Proceedings of ACL-08: HLT, pp. 254–262. Association for Computational Linguistics, Columbus (2008)

    Google Scholar 

  31. Jones, R., Ghani, R., Mitchell, T., Riloff, E.: Active learning for information extraction with multiple view feature sets. ECML-03 Workshop on Adaptive Text Extraction and Mining, Cavtat-Dubrovnik (2003)

    Google Scholar 

  32. Kaiser, K., Miksch, S.: Information extraction – a survey. Tech. Rep. Asgaard-TR-2005-6, Vienna University of Technology, Institute of Software Technology and Interactive Systems, Vienna (2005)

    Google Scholar 

  33. Lee, A., Passantino, M., Ji, H., Qi, G., Huang, T.S.: Enhancing multi-lingual information extraction via cross-media inference and fusion. In: COLING (Posters), Beijing, pp. 630–638 (2010)

    Google Scholar 

  34. Lehnert, W., Cardie, C., Fisher, D., McCarthy, J., Riloff, E., Soderland, S.: University of Massachusetts: MUC-4 test results and analysis. In: Proceedings of the 4th Message Understanding Conference. Morgan Kaufmann, McLean (1992)

    Google Scholar 

  35. Lehnert, W., Cardie, C., Fisher, D., Riloff, E., Williams, R.: University of Massachusetts: Description of the CIRCUS system as used for MUC-3. In: Proceedings of the 3rd Message Understanding Conference. Morgan Kaufmann, San Diego (1991)

    Google Scholar 

  36. Liao, S., Grishman, R.: Using document level cross-event inference to improve event extraction. In: Proceedings of ACL 2010, Uppsala, pp. 789–797. ACL (2010)

    Google Scholar 

  37. Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Human Language Technologies, Vol. 1, pp. 359–367. Association for Computational Linguistics, Stroudsburg (2011)

    Google Scholar 

  38. Llytinen, S., Gershman., A.: ATRANS: automatic processing of money transfer messages. In: Proceedings of the 5th National Conference of the American Association for Artificial Intelligence. IEEE Computer Society Press (1986)

    Google Scholar 

  39. Locke, B., Martin, J.: Named entity recognition: adapting to microblogging. Senior Thesis, University of Colorado, Colorado (2009)

    Google Scholar 

  40. Makhoul, J., Kubala, F., Schwartz, R., Weischedel, R.: Performance measures for information extraction. In: Proceedings of DARPA Broadcast News Workshop, Herndon (1999)

    Google Scholar 

  41. Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT, Cambridge, MA (1999)

    Google Scholar 

  42. Maynard, D., Tablan, V., Cunningham, H., Ursu, C., Saggion, H., Bontcheva, K., Wilks, Y.: Architectural elements of language engineering robustness. J Nat. Lang. Engin. 8(2/3), 257–274 (2002)

    Google Scholar 

  43. Mohri, M., Nederhof, M.: Regular approximation of context-free grammars through transformations. In: Junqua, J., van Noord, G. (eds.) Robustness in Language and Speech Technology, pp. 153–163. Kluwer, The Netherlands (2001)

    Google Scholar 

  44. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Linguist. Investig. 30(1), 3–26 (2007)

    Google Scholar 

  45. Neumann, G., Piskorski, J.: A shallow text processing core engine. Comput. Intell. 18, 451–476 (2002)

    Google Scholar 

  46. Ng, V., Cardie, C.: Combining sample selection and error-driven pruning for machine learning of coreference rules. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, Philadelphia, pp. 55–62 (2002)

    Google Scholar 

  47. Patwardhan, S., Riloff, E.: Effective information extraction with semantic affinity patterns and relevant regions. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 717–727 (2007)

    Google Scholar 

  48. Phillips, W., Riloff, E.: Exploiting strong syntactic heuristics and co-training to learn semantic lexicons. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) (2002)

    Google Scholar 

  49. Piskorski, J.: ExPRESS – extraction pattern recognition engine and specification suite. In: Proceedings of FSMNLP 2007 (2007)

    Google Scholar 

  50. Piskorski, J., Belayeva, J., Atkinson, M.: On refining real-time multilingual news event extraction through deployment of cross-lingual information fusion techniques. In: EISIC, pp. 38–45. IEEE (2011)

    Google Scholar 

  51. Piskorski, J., Tanev, H., Atkinson, M., van der Goot, E., Zavarella, V.: Online news event extraction for global crisis surveillance. Trans. Comput. Collectiv. Intell. (5) (2011)

    Google Scholar 

  52. Piskorski, J., Wieloch, K., Sydow, M.: On knowledge-poor methods for person name matching and lemmatization for highly inflectional languages. Inf. Retr. 12(3), 275–299 (2009)

    Google Scholar 

  53. Poibeau, T., Saggion, H. (eds.): In: Proceedings of the MMIES Workshop, RANLP: International Conference on Recent Advances in Natural Language Processing. Borovets, Bulgaria (2007)

    Google Scholar 

  54. Poibeau, T., Saggion, H., Yangarber, R. (eds.): In: Proceedings of the MMIES Workshop, COLING: International Conference on Computational Linguistics. Manchester (2008)

    Google Scholar 

  55. Recasens, M., Marquez, L., Sapena, E., Mart, A., Taule, M., Hoste, V., Poesio, M., Versley, Y.: Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval-2010), ACL 2010. Uppsala, Sweden. In: SemEval-2010 Task 1: Coreference Resolution in Multiple Languages, pp. 1–8 (2010)

    Google Scholar 

  56. Riloff, E.: Automatically constructing a dictionary for information extraction tasks. In: Proceedings of Eleventh National Conference on Artificial Intelligence (AAAI-93), Washington, DC, pp. 811–816. AAAI/MIT (1993)

    Google Scholar 

  57. Riloff, E.: Automatically generating extraction patterns from untagged text. In: Proceedings of Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, pp. 1044–1049. AAAI/MIT (1996)

    Google Scholar 

  58. Ritter, A., Clark, S., Mausam, Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), pp. 1524–1534. Association for Computational Linguistics, Edinburgh/Scotland (2011)

    Google Scholar 

  59. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of WWW 2010, Raleigh, pp. 851–860. ACM (2010)

    Google Scholar 

  60. Settles, B.: Biomedical named entity recognition using conditional random fields and rich feature sets. In: In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications, pp. 104–107. NLPBA (2004)

    Google Scholar 

  61. Shinyama, Y., Sekine, S.: Preemptive information extraction using unrestricted relation discovery. In: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, HLT-NAACL ’06, pp. 304–311. Association for Computational Linguistics, Stroudsburg (2006)

    Google Scholar 

  62. Sidner, C.L., Schultz, T., Stone, M., Zhai, C. (eds.): Multi-Document Relationship Fusion via Constraints on Probabilistic Databases. The Association for Computational Linguistics (2007)

    Google Scholar 

  63. Stevenson, M., Greenwood, M.A.: A semantic approach to IE pattern induction. In: Knight, K., Ng, H.T., Oflazer, K. (eds.) ACL. The Association for Computer Linguistics (2005)

    Google Scholar 

  64. Sudo, K., Sekine, S., Grishman, R.: Cross-lingual information extraction system evaluation. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING ’04. Association for Computational Linguistics, Stroudsburg (2004)

    Google Scholar 

  65. Tanev, H., Piskorski, J., Atkinson, M.: Real-time news event extraction for global crisis monitoring. In: Proceedings of NLDB 2008, London, pp. 207–218 (2008)

    Google Scholar 

  66. Thelen, M., Riloff, E.: A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) (2002)

    Google Scholar 

  67. Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J., Palmer, M., Schram, A., Anderson, K.: Natural language processing to the rescue? extracting “situational awareness” tweets during mass emergency. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM 2011), Barcelona, pp. 385–392. AAAI (2011)

    Google Scholar 

  68. Vieweg, S., Hughes, A., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, pp. 1079–1088. ACM, New York (2010)

    Google Scholar 

  69. Wagner, E.J., Liu, J., Birnbaum, L., Forbus, K.D., Baker, J.: Using explicit semantic models to track situations across news articles. In: Proceedings of the 2006 AAAI Workshop on Event Extraction and Synthesis, pp. 42–47 (2006)

    Google Scholar 

  70. Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques, 2nd edn. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)

    Google Scholar 

  71. Yangarber, R.: Counter-training in discovery of semantic patterns. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, Sapporo (2003)

    Google Scholar 

  72. Yangarber, R.: Verification of facts across document boundaries. In: Proceedings IIIA-2006: International Workshop on Intelligent Information Access, IIIA-2006 (2006)

    Google Scholar 

  73. Yangarber, R., Jokipii, L.: Redundancy-based correction of automatically extracted facts. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, pp. 57–64. Association for Computational Linguistics, Stroudsburg (2005)

    Google Scholar 

  74. Yangarber, R., Lin, W., Grishman, R.: Unsupervised learning of generalized names. In: Proceedings of COLING: the 19th International Conference on Computational Linguistics, Taipei (2002)

    Google Scholar 

  75. Yates, A., Banko, M., Broadhead, M., Cafarella, M.J., Etzioni, O., Soderland, S.: Textrunner: Open information extraction on the web. In: HLT-NAACL (Demonstrations), Rochester, pp. 25–26 (2007)

    Google Scholar 

  76. Zavarella, V., Tanev, H., Piskorski, J.: Event extraction for Italian using a cascade of finite-state grammars. In: Proceedings of FSMNLP 2008, Ispra (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roman Yangarber .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Piskorski, J., Yangarber, R. (2013). Information Extraction: Past, Present and Future. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28569-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28569-1_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28568-4

  • Online ISBN: 978-3-642-28569-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics