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Towards advanced collocation error correction in Spanish learner corpora

  • SI: Resources for language learning
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Abstract

Collocations in the sense of idiosyncratic binary lexical co-occurrences are one of the biggest challenges for any language learner. Even advanced learners make collocation mistakes in that they literally translate collocation elements from their native tongue, create new words as collocation elements, choose a wrong subcategorization for one of the elements, etc. Therefore, automatic collocation error detection and correction is increasingly in demand. However, while state-of-the-art models predict, with a reasonable accuracy, whether a given co-occurrence is a valid collocation or not, only few of them manage to suggest appropriate corrections with an acceptable hit rate. Most often, a ranked list of correction options is offered from which the learner has then to choose. This is clearly unsatisfactory. Our proposal focuses on this critical part of the problem in the context of the acquisition of Spanish as second language. For collocation error detection, we use a frequency-based technique. To improve on collocation error correction, we discuss three different metrics with respect to their capability to select the most appropriate correction of miscollocations found in our learner corpus.

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Notes

  1. In accordance with the terminology in Second Language Learning literature, we refer to the native tongue of the learner as ‘L1’ and to her second language as ‘L2’.

  2. Liu et al. (2009) achieve a higher accuracy, however, they start from a manually compiled list of miscollocations rather than from an automatically retrieved list.

  3. CEDEL2 is an L1 English–L2 Spanish learner corpus under construction by Cristóbal Lozano in the framework of a bigger corpus-oriented project directed by Amaya Medikoetxea at the Universidad Autónoma de Madrid. Currently, CEDEL2 contains about 730.000 words of essays in Spanish on a predefined range of topics by native speakers of English and (to a smaller extent, for contrastive studies) by native speakers of Spanish. The topics include, among others How is the region where you live?, How do your plans for the future look like?, How did you spend your last holidays?, Analyze the major aspects of immigration, and so on. The level of Spanish of the authors of the essays ranges from “elementary” over “lower intermediate”, “intermediate”, and “advanced” to “very advanced”. Further information on CEDEL2 can be obtained from http://www.uam.es/proyectoinv/woslac/cedel2.htm and (Lozano 2009; Lozano and Mendikoetxea 2013).

  4. See, e.g., (Atwell 1987; Knight and Chander 1994; Hermet et al. 2008; Meurers 2013).

  5. Recall that we use the term collocation in the sense of idiosyncratic binary lexical co-occurrences, i.e., following the lexicographic tradition (Hausmann 1989; Cowie 1994; Mel’čuk 1995).

  6. Automatic classification of miscollocations encountered in essays with respect to this typology is another big challenge, which remains to be tackled.

  7. This annotation schema is currently used to annotate a fragment of CEDEL2.

  8. The details of the filtering stage and the size of the correction list on which they calculate the reported MRR are not explicitly discussed in (Wu et al. 2010). However, we can deduce both from experiments with the MUST Collocation checker (http://miscollocation.appspot.com), which is based on their proposal.

  9. Roughly speaking, members of the same “collocation cluster” are values of the same lexical function in the sense of Mel’čuk (1995).

  10. As rightly pointed out by one of the reviewers, apart from graphic similarity, phonetic similarity should also be considered. A large number of phonetic distance measures is available; see (Kessler 2005) for an in-depth discussion—starting with the implementation of Russel and Odell’s Soundex for English.

  11. Obviously, this strategy harbors the danger of contextual feature occurrence sparseness if the learner uses a (mis)collocation in very idiosyncratic contexts.

  12. In contrast to information retrieval-oriented search, we do not eliminate from the context the functional words (which are otherwise considered to be “stop words” that do not contribute to the quality of the search) since they are essential for our task.

  13. As a matter of fact, proper names are poor features for our task. We plan to discard them in the future experiments.

  14. The suggestion *agenciar [una] cita as a possible candidate is due to the wrong PoS tagging of the bigram agencia cita ‘agency cites’, which is very common in a newspaper corpus such as ours.

  15. This gives us a hint that a newspaper material corpus is not a well-balanced corpus for the purposes of collocation-oriented CALL.

  16. However, it is a standard collocation in Argentinian Spanish.

  17. The context feature metric considered thus the same “features” as the lexical context metrics—only that it interpreted them differently.

  18. As already mentioned above, MUST is an implementation of (Wu et al. 2010).

  19. Instead of grow up mind, we introduced grow mind since MUST does not process collocations with phrasal verbs.

  20. Since MUST’s corrections for grow mind did not include any right correction, we added cultivate mind (the correction suggested by Li) to the bag of suggestions for grow mind.

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Acknowledgments

Many thanks to Amaya Medikoetxea and Cristóbal Lozano for making the CEDEL2 corpus available to us and to the two anonymous reviewers for their insightful comments, which considerably improved the final version of the paper. Our experiments have been partially run on the Argo cluster of the Department of Communication and Information Technologies, UPF. We are grateful for this service and would like to thank especially Silvina Re and Iván Jiménez for their help. This work has been partially funded by the Spanish Ministry of Science and Innovation under the contract numbers FFI2008-06479-C02-01/02 and FFI2011-30219-CO2-01/02.

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Authors and Affiliations

  1. Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain

    Gabriela Ferraro

  2. Institute for Applied Linguistics, Pompeu Fabra University, Barcelona, Spain

    Rogelio Nazar

  3. Faculty of Philology, University of La Coruña, La Coruña, Spain

    Margarita Alonso Ramos

  4. Department of Information and Communication Technologies, Catalan Institute for Research and Advanced Studies (ICREA), Pompeu Fabra University, Barcelona, Spain

    Leo Wanner

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  1. Gabriela Ferraro
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  2. Rogelio Nazar
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  4. Leo Wanner
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Correspondence to Leo Wanner.

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Ferraro, G., Nazar, R., Alonso Ramos, M. et al. Towards advanced collocation error correction in Spanish learner corpora. Lang Resources & Evaluation 48, 45–64 (2014). https://doi.org/10.1007/s10579-013-9242-3

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