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Multiword Units in Machine Translation and Translation Technology
Edited by Ruslan Mitkov, Johanna Monti, Gloria Corpas Pastor and Violeta Seretan
[Current Issues in Linguistic Theory 341] 2018
► pp. 181–200

On identification of bilingual lexical bundles for translation purposes

The case of an English-Polish comparable corpus of patient information leaflets

Grounded in phraseology and corpus linguistics, this paper aims to explore the use of bilingual lexical bundles to improve the degree of naturalness and textual fit of translated texts. More specifically, this study attempts to identify lexical bundles, that is, recurrent sequences of 3–7 words with similar discursive functions in a purpose-designed comparable corpus of English and Polish patient information leaflets, with 100 text samples in each language. Because of cross-linguistic differences, we additionally apply a number of formal criteria in order to filter out the bundles in each subcorpus. The results show that bilingual lexical bundles with overlapping discourse functions in texts and extracted from comparable corpora hold unexplored potential for machine translation, computer-assisted translation and bilingual lexicography.
Keywords: lexical bundles, comparable corpora, translation quality, translation universals, patient information leaflets
Article outline
  • 1.Introduction
  • 2.Background and related work
  • 3.Research material and methodology
  • 4.Results
  • 5.Discussion and conclusions
  • Notes
  • References
  • Appendix
Published online: 20 July 2018
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https://doi.org/10.1075/cilt.341.09gra
References (48)
References
Allschwil: The European Association for Machine Translation. Available at: [URL] (accessed November 2014)
Baker, M. (1993). Corpus linguistics and translation studies: Implications and applications. In M. Baker, G. Francis, & E. Toginini-Bonelli (Eds.), Text and Technology. In Honor of John Sinclair (pp.233–250). Amsterdam: John Benjamins. DOI logoGoogle Scholar
(1996). Corpus-based translation studies: The challenges that lie ahead. In H. Somers (Ed.), Terminology, LSP and Translation: Studies in Language Engineering. In Honour of Juan C. Sager (pp.175–186). Amsterdam: John Benjamins. DOI logoGoogle Scholar
Barreiro, A., Monti, J., Batista F. & Orliac B. (2013). When Multiwords Go Bad in Machine Translation. In J. Monti, R. Mitkov, G. Corpas-Pastor, & V. Seretan (Eds.), Workshop Proceedings: Multi-Word Units in Machine Translation and Translation Technologies (pp.26–33). Google Scholar
Biber, D. (2006). University Language. A corpus-based study of spoken and written registers. Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
(2009). A corpus-driven approach to formulaic language in English: multi-word patterns in speech and writing. International Journal of Corpus Linguistics, 14 (3), 275–311. DOI logoGoogle Scholar
Biber, D., S. Johansson, G. Leech, S. Conrad & Finegan, E. (1999). The Longman Grammar of Spoken and Written English. London: Longman.Google Scholar
Biber, D., Conrad, S. & Cortes, V. (2003). Lexical bundles in speech and writing: An initial taxonomy. In A. Wilson, P. Rayson, & T. McEnery (Eds.), Corpus Linguistics by the Lune: A Festschrift for Geoffrey Leech (pp.71–92). Frankfurt am Main: Peter Lang.Google Scholar
(2004). “If you look at…: Lexical bundles in university teaching and textbooks”. Applied Linguistics, 25(3), 371–405. DOI logoGoogle Scholar
Biel, Ł. (2014). Lost in the Eurofog. The Textual Fit of Translated Law. Frankfurt am Main: Peter Lang. DOI logoGoogle Scholar
Bouayad-Agha, N. (2006) The Patient Information Leaflet (PIL) corpus. Available at: [URL] (accessed May 2012).Google Scholar
Callison-Burch, Ch., Fordyce, C., Koehn, P., Monz, Ch. & Schroeder, J. (2007). (Meta-) Evaluation of Machine Translation. StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation, Association for Computational Linguistics, 136–158. Available at: [URL] (accessed February 2015).
(2008). Further Meta-Evaluation of Machine Translation. StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation, Association for Computational Linguistics, 70–106. Available at: [URL] (accessed February 2015).DOI logo
Chen, Y.-H. & Baker, P. (2010). Lexical bundles in L1 and L2 academic writing. Language Learning and Technology, 14(2), 30–49.Google Scholar
Cheng., W, Greaves, C. & Warren, M. (2006). From n-gram to skipgram to concgrams. International Journal of Corpus Linguistics, 11(4), 411–433. DOI logoGoogle Scholar
Chesterman, A. (2004). Hypothesis about translation universals. In G. Hansen, K. Malmkjaer, & D. Gile (Eds.), Claims, Changes and Challenges in Translation Studies (pp.1–13). Amsterdam: John Benjamins. DOI logoGoogle Scholar
Cobb, T. (2003). Review: Alison Wray. 2001. Formulaic Language and the Lexicon. Cambridge: Cambridge. University Press. xi + 332pp. Canadian Journal of Applied Linguistics, 6 (1), 105–110.Google Scholar
di Buono, M., Monti, J., Monteleone, M. & Marano, F. (2013). Multiword processing in an ontology-based Cross-Language Information Retrieval model for specific domain collections. In J. Monti, R. Mitkov, G. Corpas-Pastor, & V. Seretan (Eds.), Workshop Proceedings: Multi-Word Units in Machine Translation and Translation Technologies (pp.43–52). Allschwil: The European Association for Machine Translation. Available at: [URL] (accessed November 2014).Google Scholar
Farwell, D., Guthrie, L. & Wilks, Y. (1993). Automatically Creating Lexical Entries for ULTRA, a Multilingual MT System. Machine Translation, 8, 127–145. DOI logoGoogle Scholar
Forchini, P. & Murphy, A. (2008). N-grams in comparable specialized corpora. Perspectives on phraseology, translation and pedagogy. International Journal of Corpus Linguistics, 13(3), 351–367. DOI logoGoogle Scholar
Frantzi, K., Ananiadou, S. & Mima, H. (2000). Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal on Digital Libraries, 3(2), 115–130. DOI logoGoogle Scholar
Goźdź-Roszkowski, S. (2011). Patterns of Linguistic Variation in American Legal English. A Corpus-Based Study. Frankfurt am Main: Peter Lang Verlag. DOI logoGoogle Scholar
Grabowski, Ł. (2013). Interfacing corpus linguistics and computational stylistics: translation universals in translational literary Polish. International Journal of Corpus Linguistics, 18(2), 254–280. DOI logoGoogle Scholar
(2014). On Lexical Bundles in Polish Patient Information Leaflets: A Corpus-Driven Study. Studies in Polish Linguistics, 9(1), 21–43.Google Scholar
(2015). Keywords and lexical bundles within English pharmaceutical discourse: a corpus-driven description. English for Specific Purposes, 38, 23–33. DOI logoGoogle Scholar
Granger, S. (2010). Comparable and translation corpora in cross-linguistic research. Design, analysis and applications. Journal of Shanghai Jiaotong University, 2, 14–21. Available at: [URL] (accessed November 2014).Google Scholar
(2014). A lexical bundle approach to comparing languages. Stems in English and French. In M.-A. Lefer, & S. Vogeleer (Eds.), Genre- and register-related discourse features in contrast. Special issue of Languages in Contrast , 14(1), 58–72.Google Scholar
Gray, B. & Biber, D. (2013). Lexical frames in academic prose and conversation. International Journal of Corpus Linguistics, 18(1), 109–135. DOI logoGoogle Scholar
Hoey, M. (2005). Lexical Priming: A New Theory of Words and Language. London: Routledge.DOI logoGoogle Scholar
(2007). Lexical priming and literary creativity. In M. Hoey, M. Mahlberg, M. Stubbs, & W. Teubert (Eds.), Text, Discourse and Corpora. London: Continuum, 7–30.Google Scholar
Hoang, H. & Koehn, P. (2008). Design of the Moses Decoder for Statistical Machine Translation. Software Engineering, Testing, and Quality Assurance for Natural Language Processing (pp.58–65). Columbus, Ohio, USA, June (2008). Association for Computational Linguistics. Available at: [URL] (accessed November 2014).Google Scholar
Hyland, K. (2008). As can be seen: Lexical bundles and disciplinary variation. English for Specific Purposes, 27, 4–21. DOI logoGoogle Scholar
Kajzer-Wietrzny, M. (2012). Interpreting Universals and Interpreting Style. Unpublished PhD dissertation. Adam Mickiewicz University, Poznań, Poland. Available at: [URL] (accessed September 2012).Google Scholar
Kilgarriff, A. (2005). Language is never ever ever random. Corpus Linguistics and Linguistic Theory, 1(2), 263–276. DOI logoGoogle Scholar
Koehn, P., Hoang, H., Birch, A., Callison-Burch, Ch., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, Ch., Zens, R., Dyer, Ch., Bojar, O., Constantin, A. & Herbst, E. (2007). Moses: Open Source Toolkit for Statistical Machine Translation. Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, June 2007. Available at: [URL] (accessed November 2014).DOI logo
Laviosa, S. (1998). Core patterns of lexical use in a comparable corpus of English narrative prose. Meta, 43(4), 557–570. DOI logoGoogle Scholar
Montalt Resurreccio, V. & Gonzalez Davies, M. (2007). Medical Translation Step by Step. Translation Practices explained. Manchester: St. Jerome Publishing.Google Scholar
Olohan, M. (2004). Introducing Corpora in Translation Studies. London/New York: Routledge.Google Scholar
Olohan, M. & Baker, M. (2000). Reporting that in translated English: Evidence for subconscious processes of explicitation?. Across Languages and Cultures, 1, 141–172 (cited in Olohan 2004: 94). DOI logoGoogle Scholar
Papineni, K., Roukos, S., Ward, T., Zhu, W-J. (2002). BLEU: a method for automatic evaluation of machine translation. Proceedings for the 40th Annual Meeting of the Association for Computation Linguistics, Philadelphia, July 2002. (pp.311–318). Available at: [URL] (accessed November 2014).
Ren, Z., Lu, Y., Cao, J., Liu, Q. & Huang, Y. (2009). Improving Statistical Machine Translation Using Domain Bilingual Multiword Expressions. Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications. MWE’ 09. (pp.47–54). Stroudsburg: Association for Computational Linguistics. Available at: [URL] (accessed November 2014).DOI logoGoogle Scholar
Sag, I., Baldwin, T., Bond, F., Copestake, A., & Flickinger D. (2002). Multiword Expressions: A Pain in the Neck for NLP. Computational Linguistics and Intelligent Text Processing: Third International Conference (CICLing 2002), 1–15. Available at: [URL] (accessed May 2013).
Salazar, D. (2011). Lexical bundles in scientific English: A corpus-based study of native and non-native writing. Unpublished PhD dissertation. University of Barcelona. Available at: [URL] (accessed March 2013)Google Scholar
Scott, D., Bouayad-Agha, N., Power, R., Shultz, S., Beck, R., Murphy, D. & Lockwood, R.. (2001). PILLS: A Multilingual Authoring System for Patient Information. Proceedings of the 2001 Meeting of the American Medical Informatics Association (AMAI'01), Washington, D.C., USA. Available at: [URL] (accessed May 2013).Google Scholar
Scott, M. (2007). WordSmith Tools 4.0. Liverpool: Lexical Analysis Software.Google Scholar
Stubbs, M. & Barth, I. (2003). Using recurrent phrases as text-type discriminators: a quantitative method and some findings. Functions of Language, 10(1), 65–108. DOI logoGoogle Scholar
White, J. (2003). How to evaluate machine translation. In H. Somers (Ed.), Computers and Translation: A Translator’s Guide (pp.211–244). Amsterdam: John Benjamins. DOI logoGoogle Scholar
Wilks, Y. (2009). Machine Translation: Its Scope and Limits. New York: Springer.Google Scholar
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