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Learning Clusters of Bilingual Suffixes Using Bilingual Translation Lexicon

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9468)

Abstract

By learning bilingual suffixation operations from translations using an existing bilingual lexicon with near translation forms we can improve its coverage and hence deal with the OOV entries. From this perspective, we identify bilingual stems, their bilingual morphological extensions (bilingual suffixes) and subsequently clusters of bilingual suffixes using known translation forms seen in an existing bilingual translation lexicon. We rely on clustering to enable safer translation generalisations. The degree of co-occurrence between two bilingual morphological extensions with reference to common bilingual stems determines if each of them should fall in the same cluster. Results are discussed for language pairs English-Portuguese (EN-PT) and English-Hindi (EN-HI).

Keywords

  • Language Pair
  • Bilingual Lexicon
  • Bilingual Translation
  • Translation Form
  • Natural Language Processing System

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    Note the null suffix in EN corresponding to gender and number suffixes in HI.

  2. 2.

    Translations that are lexically similar.

  3. 3.

    A suffix cluster may or may not correspond to Part-of-Speech such as noun or adjective but there are cases where the same suffix cluster aggregates nouns, adjectives and adverbs.

  4. 4.

    http://glaros.dtc.umn.edu/gkhome/views/cluto

  5. 5.

    2\(^{nd}\) line in each row shows the transliterations for HI terms.

  6. 6.

    http://sanskritdocuments.org/hindi/dict/eng-hin\(\_\)unic.html/ www.dicts.info www.hindilearner.com

  7. 7.

    EMILLE Corpus - http://www.emille.lancs.ac.uk/

  8. 8.

    DGT-TM - https://open-data.europa.eu/en/data/dataset/dgt-translation-memory Europarl - http://www.statmt.org/europarl/ OPUS (EUconst, EMEA) - http://opus.lingfil.uu.se/

  9. 9.

    In the Table 4, only two bilingual suffixes are shown per cluster although the original clusters contains varying number of bilingual suffixes ranging from 2 to 15 for EN-PT and from 2 to 5 for EN-HI.

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Acknowledgements

K.M. Kavitha and Luís Gomes acknowledge the Research Fellowship by FCT/MCTES with Ref. nos., SFRH/BD/64371/2009 and SFRH/BD/65059/2009, respectively, and the funded research project ISTRION (Ref. PTDC/EIA-EIA/114521/2009) that provided other means for the research carried out. The authors thank NOVA LINCS, FCT/UNL for the support, SJEC for providing the financial assistance to participate in MIKE 2015, and ISTRION BOX - Translation&Revision, Lda., for providing the data and valuable consultation.

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Kavitha, K.M., Gomes, L., Lopes, J.G.P. (2015). Learning Clusters of Bilingual Suffixes Using Bilingual Translation Lexicon. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_57

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_57

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