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Information Extraction: Past, Present and Future

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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.

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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.

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  1. Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Jakub Piskorski

  2. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Roman Yangarber

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  1. Universite Sorbonne Nouvelle, LATTICE-CNRS, Ecole Normale Superieure and, rue d'Ulm 45, Paris, 75005, France

    Thierry Poibeau

  2. , Information & Communication Technologies, Universitat Pompeu Fabra, C/ Tanger 122-140, Barcelona, 08018, Spain

    Horacio Saggion

  3. Institute for Computer Science, Polish Acadmey of Science, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland

    Jakub Piskorski

  4. Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2, Helsinki, 00014, Finland

    Roman Yangarber

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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

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