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Large Scale Text Mining Approaches for Information Retrieval and Extraction

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Innovations in Intelligent Machines-4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 514))

Abstract

The issues for Natural Language Processing and Information Retrieval have been studied for long time but the recent availability of very large resources (Web pages, digital documents…) and the development of statistical machine learning methods exploiting annotated texts (manual encoding by crowdsourcing is a new major way) have transformed these fields. This allows not limiting these approaches to highly specialized domains and reducing the cost of their implementation. For this chapter, our aim is to present some popular text-mining statistical approaches for information retrieval and information extraction and to discuss the practical limits of actual systems that introduce challenges for future.

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Notes

  1. 1.

    Alternatives to the use of probability and to Bayesian networks or other probabilistic graphic models for dealing with uncertainty have been proposed. Among them fuzzy logic and Dempster-Shafer theory.

  2. 2.

    Precision is the fraction of retrieved items that are relevant or well classified while recall is the fraction of relevant items that are retrieved and provided as result. F-score is the harmonic mean of precision and recall.

  3. 3.

    Stemming consists in reducing words according to their morphological variants and roots. See for example Snowball that makes light stemming available for many languages (http://snowball.tartarus.org). Lemmatization can be seen as an advanced stemming.

  4. 4.

    Google Books Ngram (http://books.google.com/ngrams) and n-grams from the Corpus of Contemporary American English COCA (http://www.ngrams.info/) are two popular and freely downloadable word n-grams sets for English.

  5. 5.

    http://trec.nist.gov/tracks.html

  6. 6.

    http://ilps.science.uva.nl/trec-entity/

  7. 7.

    During CoNLL 2003 (Conference on Computational Natural Language Learning) a challenge that concerned language-independent named entity recognition was organized. Many other tasks related to Natural Language Processing have been organized in the context of CoNLL conferences: grammatical error correction, multilingual parsing, analysis of dependencies… (http://www.clips.ua.ac.be/conll/).

  8. 8.

    Freebase (https://developers.google.com/freebase/) contains in June 2013 more than 37 million entities, 1,998 types and 30,000 properties.

  9. 9.

    http://nlp.stanford.edu/software/CRF-NER.shtml

  10. 10.

    http://trec.nist.gov/tracks.html

  11. 11.

    LDC catalog number LDC2002T31 (http://www.ldc.upenn.edu).

  12. 12.

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

  13. 13.

    http://ir.dcs.gla.ac.uk/test_collections/blog06info.html (about 40 GB of data for feeds only).

  14. 14.

    http://www.inex.otago.ac.nz/tracks/qa/qa.asp.

  15. 15.

    https://inex.mmci.uni-saarland.de/tracks/qa/.

  16. 16.

    http://wordnet.princeton.edu.

  17. 17.

    http://lucene.apache.org.

  18. 18.

    http://sentiwordnet.isti.cnr.it.

  19. 19.

    http://www.cs.york.ac.uk/semeval-2013/.

  20. 20.

    DBPedia is a large knowledge base (more than 3.77 million things are classified in an ontology) localized in 111 languages built by extracting structured information from Wikipedia (http://dbpedia.org)—June 2013.

  21. 21.

    http://trec.nist.gov/data/kba.html.

  22. 22.

    http://www.nist.gov/tac/publications/index.html.

  23. 23.

    http://lab.hypotheses.org

  24. 24.

    http://openedition.org

  25. 25.

    Text Encoding Initiative (http://www.tei-c.org/Guidelines/).

  26. 26.

    This project was supported by the 6th Framework Research Programme of the European Union (EU), Project LUNA, IST contract no 33549 (www.ist-luna.eu).

  27. 27.

    https://framenet.icsi.berkeley.edu/fndrupal/.

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Authors and Affiliations

  1. CNRS, Aix-Marseille Université, LSIS UMR 7296, Av. Esc. Normandie-Niemen, 13397, Marseille cedex 20, France

    Patrice Bellot, Vincent Bouvier & Young-Min Kim

  2. iSmart, 565 rue M. Berthelot, 13851, Aix-en-Provence cedex 3, France

    Ludovic Bonnefoy, Vincent Bouvier & Frédéric Duvert

  3. LIA, Université d’Avignon et des Pays de Vaucluse, Agroparc, 84911, Avignon cedex 9, France

    Ludovic Bonnefoy

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  1. Patrice Bellot
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  2. Ludovic Bonnefoy
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  3. Vincent Bouvier
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  4. Frédéric Duvert
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  5. Young-Min Kim
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Correspondence to Patrice Bellot .

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  1. Ecole Polytechnique Universitaire de Marseille, Aix-Marseille University (AMU), Marseille, France

    Colette Faucher

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Bellot, P., Bonnefoy, L., Bouvier, V., Duvert, F., Kim, YM. (2014). Large Scale Text Mining Approaches for Information Retrieval and Extraction. In: Faucher, C., Jain, L. (eds) Innovations in Intelligent Machines-4. Studies in Computational Intelligence, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-319-01866-9_1

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