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International Conference on Computational Processing of the Portuguese Language

PROPOR 2016: Computational Processing of the Portuguese Language pp 319–329Cite as

Towards a Statistical-Enriched Corpus Containing Portuguese Collocations in Use: Reviewing Possible Extraction Tools

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9727))

Abstract

Collocations are a main problem for any natural language processing task, from machine translation to summarization. With the goal of building a corpus with collocations, enriched with statistical information about them, we survey, in this paper, four tools for extracting collocations. These tools allow us to collect sentences with collocations, and also to gather statistics on this particular type of co-ocurrences, like Mutual Information and Log likelihood values.

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Notes

  1. 1.

    http://corpora.informatik.uni-leipzig.de.

  2. 2.

    https://gramtrans.com/deepdict/.

  3. 3.

    http://www.clul.ul.pt/pt/recursos/.

  4. 4.

    https://www.sketchengine.co.uk.

  5. 5.

    http://www.linguateca.pt/COMPARA/listas_freq.php.

  6. 6.

    http://beta.visl.sdu.dk/constraint_grammar.html.

  7. 7.

    http://linguateca.dei.uc.pt/Floresta/InicialFloresta.html.

  8. 8.

    http://www.linguateca.pt/floresta/principal.html.

  9. 9.

    This measure is based only on a frequency of words w1 and w2 and bigram w1 w2, it is not affected by the size of the corpus.

  10. 10.

    If the clustering option is selected, the collocates within a word sketch are clustered according to any such clusters from the distributional thesaurus that they appear in. The words from the thesaurus are clustered according to their distributional similarity scores.

  11. 11.

    Salience is a statistical measure of how salient a word or lemma is in a given context, given the frequency of the word and the context. This is measured with logDice.

  12. 12.

    [7] suggests that MI is generally used for a lexicographical purpose, while MI3 is probably more useful for second language learning.

References

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Acknowledgments

The work was partially supported by national funds through FCT - Fundação para a Ciência e a Tecnologia, reference UID/CEC/50021/2013. Ângela Costa is supported by PhD fellowship from FCT (SFRH/BD/85737/2012).

Author information

Authors and Affiliations

  1. INESC-ID Lisboa, Lisbon, Portugal

    Ângela Costa & Luísa Coheur

  2. Instituto Superior Técnico, Lisbon, Portugal

    Luísa Coheur

  3. Centro de Linguística da, Universidade Nova de Lisboa, Lisbon, Portugal

    Ângela Costa

Authors
  1. Ângela Costa
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  2. Luísa Coheur
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Corresponding author

Correspondence to Ângela Costa .

Editor information

Editors and Affiliations

  1. Universidade de Lisbon, Portugal

    João Silva

  2. ISCTE-IUL, Lisbon, Portugal

    Ricardo Ribeiro

  3. Universidade de Évora, Évora, Portugal

    Paulo Quaresma

  4. Universidade de Caxias do Sul, Caxias do Suö, Brazil

    André Adami

  5. Universidade de Lisbon, Lisboa, Portugal

    António Branco

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Costa, Â., Coheur, L. (2016). Towards a Statistical-Enriched Corpus Containing Portuguese Collocations in Use: Reviewing Possible Extraction Tools. In: Silva, J., Ribeiro, R., Quaresma, P., Adami, A., Branco, A. (eds) Computational Processing of the Portuguese Language. PROPOR 2016. Lecture Notes in Computer Science(), vol 9727. Springer, Cham. https://doi.org/10.1007/978-3-319-41552-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-41552-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41551-2

  • Online ISBN: 978-3-319-41552-9

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