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State of the Art in MWE Processing

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Multiword Expressions Acquisition

Abstract

In the previous chapter, we provided the historical and theoretical foundations for the study of multiword expressions. The set of definitions, characteristics and types described give an idea of the difficulty of the computational tasks involving MWEs. The goal of the present chapter is to draw an overview of the state of the art in computational methods for MWE treatment, focusing on acquisition. State-of-the-art techniques to deal with MWEs are the starting point of the methodology proposed in Chap. 5. Information contained in the present chapter allows better comparison and contextualisation of the present work in the computational linguistics panorama.

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Notes

  1. 1.

    The goal of this section is not to provide a substantial introduction to empirical methods in computational linguistics. Instead, we remind and try to disambiguate as much as possible the definitions of concepts that are already familiar to the reader to some extent. If this is not the case, we recommend Jurafsky and Martin (2008) as a consolidated and wide introduction to NLP and Manning and Schütze (1999) for a more specific introduction to empirical methods. Our text is inspired by these two standard reference textbooks.

  2. 2.

    Contraction identification usually requires context-aware analysis. For instance, in French, the contraction \(\mathit{des} = \mathit{de} + \mathit{les}\) is homonym to the partitive/indefinite article des.

  3. 3.

    We use the character ˽ only to emphasise the spaces between words.

  4. 4.

    However, it is not enough to lowercase the whole text as case information may be important, for instance, in domain-specific texts (chemical element NaCl), acronyms (NASA, CIA) and to distinguish named entities (Bill Gates, March) from common words (pay the bill, open the gates, the soldiers march).

  5. 5.

    The tagset used by the TreeTagger in English is available at ftp://ftp.cis.upenn.edu/pub/treebank/doc/tagguide.ps.gz and reproduced in Appendix D.4.

  6. 6.

    Actually, RASP does not generate dependency relations directly, but it infers grammatical relations using equivalence rules applied to a traditional constituent parsing tree. Relations are mostly acyclic and exceptions can be dealt with on a case by case basis.

  7. 7.

    Documentation about RASP’s tagset and grammatical relations is available at http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-662.pdf and in Appendix C of Jurafsky and Martin (2008). Moreover, the tags used by RASP for POS and syntax are reproduced in Appendices D.2 and D.3.

  8. 8.

    This is a simplification, as described by Briscoe et al. (2006).

  9. 9.

    The type/token ration, that is, the number of types with respect to the number of tokens in a text, has been used as a measure of the richness of the vocabulary. This measure depends on the corpus size (Baayen 2001). In BNC-frg, the type/token ratio is of 0.091.

  10. 10.

    A word occurring once in the corpus is called a hapax, from the Greek hapax legomena.

  11. 11.

    Discontiguous sequences are sometimes referred to as flexigrams, that is, n-grams with gaps.

  12. 12.

    The term association measure is standard in MWE acquisition, but it would be more appropriate to talk about association scores instead, since not all the scores discussed here are proper measures.

  13. 13.

    The test statistic is a random variable with a known distribution, from which we can obtain the p-value. If the p-value is below a certain significance level, we can reject the null hypothesis.

  14. 14.

    http://multiword.sourceforge.net/mwe2009

  15. 15.

    Recommended by the author of the algorithm in personal communication.

  16. 16.

    Although this can be simulated by concatenating words and POS tags together in order to form a token.

  17. 17.

    http://olst.ling.umontreal.ca/~drouinp/termostat_web/

  18. 18.

    http://www.antlab.sci.waseda.ac.jp/software.html

  19. 19.

    http://www.nactem.ac.uk/software/termine/

  20. 20.

    http://en.wikipedia.org/wiki/Terminology_extraction

  21. 21.

    http://mwetoolkit.sourceforge.net

  22. 22.

    http://www.temis.com/

  23. 23.

    http://www.temis.com/index.php?id=201&selt=1

  24. 24.

    http://developer.yahoo.com/search/content/V1/termExtraction.html

  25. 25.

    http://129.194.38.128:81/FipsCoView

  26. 26.

    http://similis.org/

  27. 27.

    A noun derived from a verb, like replacement is a nominalisation of the verb replace.

  28. 28.

    The context unit used for annotation was the sentence. However, due to anaphora, sometimes it was impossible to know the intended meaning without looking at neighbour sentences.

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Ramisch, C. (2015). State of the Art in MWE Processing. In: Multiword Expressions Acquisition. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-09207-2_3

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