Cover not available
Part of
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. 1–38

Multiword units in machine translation and translation technology

The correct interpretation of Multiword Units (MWUs) is crucial to many applications in Natural Language Processing but is a challenging and complex task. In recent years, the computational treatment of MWUs has received considerable attention but we believe that there is much more to be done before we can claim that NLP and Machine Translation (MT) systems process MWUs successfully. In this chapter, we present a survey of the field with particular reference to Machine Translation and Translation Technology.
Keywords: multiword units, multiword expressions, natural language processing, machine translation, translation technology
Article outline
  • 1.Introduction
  • 2.Multiword units in natural language processing
    • 2.1Historical notes
    • 2.2POS tagging and parsing
    • 2.3Word sense disambiguation
    • 2.4Information extraction and information retrieval
    • 2.5Other applications
  • 3.Multiword unit processing in machine translation
    • 3.1Historical notes
    • 3.2Multiword unit processing in RBMT
    • 3.3Multiword unit processing in EBMT
    • 3.4Multiword unit processing in SMT
  • 4.Multiword units in translation technology
  • Acknowledgements
  • References
  • Notes
Published online: 20 July 2018
DOI logo
https://doi.org/10.1075/cilt.341.01mon
References (192)
References
Abeillé, A., Clément, L., & Toussenel, F. (2003). Building a treebank for French. In Abeillé(Ed.) Treebanks (pp.165–187). Dordrecht: Kluwer. DOI logoGoogle Scholar
Acosta, O., Villavicencio, A., & Moreira, V. (2011). Identification and treatment of multiword expressions applied to Information Retrieval. In Proceedings of the workshop on multiword expressions: From parsing and generation to the real world (pp.101–109). Portland, Oregon, USA.Google Scholar
Alegría, I., Ansa, O., Artola, X., Ezeiza, N., Gojenola, K., & Urizar, R. (2004). Representation and treatment of multiword expressions in Basque. Second ACL workshop on multiword expressions: Integrating processing (pp.48–55). Barcelona, Spain. DOI logoGoogle Scholar
Anastasiou, D. (2009). Idiom treatment experiments in machine translation (Unpublished doctoral dissertation). Saarland University.Google Scholar
(2010). Idiom treatment experiments in machine translation. Newcastle upon Tyne: Cambridge Scholars Publishing.Google Scholar
Arranz, V., Atserias, J., & Castillo, M. (2005). Multiwords and word sense disambiguation. In Proceedings Computational linguistics and intelligent text processing: 6th international conference, CICLING 2005, Mexico City, Mexico, February 13–19, 2005. (pp.250–262). Mexico city, Mexico.DOI logoGoogle Scholar
Arnold, I.V. 1973. The English Word Moscow: Higher School Publishing HouseGoogle Scholar
Aziz, W., Dymetman, M., Mirkin, S., Specia, L., Cancedda, N., & Dagan, I. (2010). Learning an expert from human annotations in statistical machine translation: The case of out-of-vocabulary words. In Proceedings of the 14th annual meeting of the European Association for Machine Translation (EAMT) (pp.28–35). Saint-Rapha, France.Google Scholar
Baldwin, T. (2011). MWEs and topic modelling: Enhancing machine learning with linguistics. In Proceedings of the workshop on multiword expressions: From parsing and generation to the real world (p.1). Portland, Oregon, USA.Google Scholar
Baldwin, T., & Kim, S. N. (2010). Multiword expressions. In N. Indurkhya & F. J. Damerau, (Eds.), Handbook of Natural Language Processing, Second Edition (pp.267–292). Boca Raton, USA: Chapman and Hall/CRC (2010).Google Scholar
Bar-Hillel, Y. (1952). “The Treatment of ‘idioms’ by a Translating Machine”, presented at the Conference on Mechanical Translation at Massachusetts Institute of Technology, June 1952.
Barreiro, A., & Batista, F. (2016). Machine translation of non-contiguous multiword units. In Proceedings of Workshop on Discontinuous Structures in Natural Language Processing (DiscoNLP) (pp.22–30). San Diego, California, USA.
Barreiro, A., Monti, J., Orliac, B., Preuß, S., Arrieta, K., Ling, W., Batista, F. & Trancoso, I. (2014). Linguistic evaluation of support verb constructions by OpenLogos and Google Translate. In Proceedings of Ninth International Conference on Language Resources and Evaluation (LREC2014) (pp.35–40). Reykjavik, Island.
Barreiro, A., Raposo, F., & Luís, T. (2016). CLUE-Aligner: An alignment tool to annotate pairs of paraphrastic and translation units. In Proceedings of the LREC 2016 Workshop “Translation Evaluation: From Fragmented Tools and Data Sets to an Integrated Ecosystem” (pp.7–13). Portorož, SloveniaGoogle Scholar
Biber, D., Johansson, S., Leech, G., Conrad, S., & Finegan, E. (1999). Grammar of spoken and written English. Edimburgh: Pearson Education Limited.Google Scholar
Boonthum, C., Toida, S., & Levinstein, I. (2005). Sense disambiguation for preposition with . In Proceedings of the second ACL–SIGSEM workshop on the linguistic dimensions of prepositions and their use in computational linguistic formalisms and applications (pp.153–162). Colchester, United Kingdom.Google Scholar
Bouamor, D., Semmar, N., Zweigenbeaum, P., (2012), Automatic Construction of a MultiWord Expressions Bilingual Lexicon: A Statistical Machine Translation Evaluation Perspective, Proceedings of the 3rd Workshop on Cognitive Aspects of the Lexicon (CogALex-III), COLING 2012. (pp.95–108). Mumbai, India.Google Scholar
Bouamor, D., Semmar, N., & Zweigenbaum, P. (2011). Improved statistical machine translation using multiword expressions. In Proceedings of the International Workshop on Using Linguistic Information for Hybrid Machine Translation (LIHMT 2011) (pp.15–20). Barcelona, Spain.Google Scholar
Boulaknadel, S., Daille, B., & Aboutajdine, D. (2008). A multi-word term extraction program for Arabic language. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08) (pp.1485–1488). Marrakech, Morocco.Google Scholar
Brooke, J., Hammond, A., Jacob, D., Tsang, V., Hirst, G., & Shein, F. (2015). Building a lexicon of formulaic language for language learners. In Proceedings of the 11th workshop on multiword expressions (pp.96–104). Denver, Colorado, USA.DOI logoGoogle Scholar
Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D., Mercer R. L. & Roossin, P. S. (1990). A statistical approach to machine translation. Computational linguistics, 16(2), 79–85.Google Scholar
Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational linguistics, 19(2), 263–311.Google Scholar
Brown, P., Cocke, J., Pietra, S. D., Pietra, V. D., Jelinek, F., Mercer, R., & Roossin, P. (1988). A statistical approach to language translation. In Proceedings of the 12th conference on Computational linguistics, Volume 1, (pp.71–76). Budapest, Hungry.DOI logoGoogle Scholar
Brun, C. (1998). Terminology finite-state preprocessing for computational LFG. In Proceedings of the 36th annual meeting of the association for computational linguistics and 17th international conference on computational linguistics (pp.196–200). Morristown, New Jersey, USA.Google Scholar
Burstein, J. (2013). The far reach of multiword expressions in educational technology. In Proceedings of the 9th workshop on multiword expressions (p.138). Atlanta, Georgia, USA.Google Scholar
Cacciari, C., & Tabossi, P. 1988. The comprehension of idioms. Journal of Memory and Language, 27(6), 668–683 DOI logoGoogle Scholar
Cap, F., Nirmal, M., Weller, M. & Schulte im Walde, S. (2015), How to Account for Idiomatic German Support Verb Constructions in Statistical Machine Translation. In Proceedings of the 11th Workshop on Multiword Expressions (MWE) at NAACL (pp.19–28). Denver, Colorado, USA.DOI logoGoogle Scholar
Cap, F. (2014). Morphological processing of compounds for statistical machine translation (Unpublished doctoral dissertation). University of Stuttgart.Google Scholar
Carpuat, M., & Diab, M. (2010). Task-based evaluation of multiword expressions: A pilot study in statistical machine translation. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp.242–245). Los Angeles, California, USA.Google Scholar
Chafe, W. 1968. Idiomaticity as an anomaly in the Chomskyan paradigm. Foundations of Language 4. 109–127.Google Scholar
Carter, R. 1998. Vocabulary: Applied Linguistics Perspectives (2nd ed.) London and New York: Routledge.DOI logoGoogle Scholar
Chiang, D. (2005). A hierarchical phrase-based model for statistical machine translation. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp.263–270). Ann Arbor, Michigan, USAGoogle Scholar
Cho, K. Van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. In Proceedings of Conference on Empirical Methods on Natural Language Processing (EMNLP 2014) (pp.1724–1734). Doha, Qatar.DOI logoGoogle Scholar
Cho, K. (forthcoming) ‘Deep Learning’. In Mitkov, R. (Ed.) The Oxford Handbook of Computational Linguistics, 2nd ed. Oxford: Oxford University Press.
Chomsky, N. (1980). Rules and representations. Behavioral and brain sciences, 3(1), 1–15.DOI logoGoogle Scholar
Choueka, Yaacov, S.T Klein & E. Neuwitz. 1983. “Automatic Retrieval of Frequent Idiomatic and Collocational Expressions in a Large Corpus”. Journal of the Association for Literary and Linguistic Computing 4 (1). 34–38.Google Scholar
Claveau, V. (2009). Translation of biomedical terms by inferring rewriting rules. In Prince, V. (Ed.). Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, IGI-Global (pp.106–123). DOI logoGoogle Scholar
Colson, J. P. (forthcoming). “Computational phraseology and translation studies: from theoretical hypotheses to practical tools.” In Corpas Pastor, G. Colson, J. P. & Heid, U. (Eds.). (forthcoming). Computational Phraseology. Amsterdam & New York: John Benjamins
(2016) “Set phrases around globalization : an experiment in corpus-based computational phraseology. In Input a Word, Analyze the World. Selected Approaches to Corpus Linguistics, Ed. by F.A Almeida, I. Ortega Barrera, E. Quintana Toledo, and M.E. Sánchez Cuervo, 141–152. Newcastle: Cambridge Scholars Publishing.Google Scholar
Constant, M., & Sigogne, A. (2011). MWU-aware part-of-speech tagging with a CRF model and lexical resources. In Proceedings of the workshop on multiword expressions: From parsing and generation to the real world (pp.49–56). Portland, Oregon, USA.Google Scholar
Constant, M. Candito, M. & Seddah, D. (2013b) The LIGM-Alpage Architecture for the SPMRL 2013 Shared Task: Multiword Expression Analysis and Dependency Parsing. Shared task track of the EMNLP Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL’13) (pp.46–52). Seattle, Washington, USA.Google Scholar
Constant, M., Eryiğit, G., Monti, J., Van Der Plas, L., Ramisch, C., Rosner, M., & Todirascu, A. (2017). Multiword expression processing: a survey. Computational Linguistics, 43(4), 837–892.DOI logoGoogle Scholar
Constant, M., Roux, J. L., & Sigogne, A. (2013a). Combining compound recognition and PCFG-LA parsing with word lattices and conditional random fields. ACM Transactions on Speech and Language Processing (TSLP), 10 (3), 8:1–8:24.Google Scholar
Cook, P., & Hirst, G. (2013). Automatically assessing whether a text is clichéd, with applications to literary analysis. In Proceedings of the 9th workshop on multiword expressions (pp.52–57). Atlanta, Georgia, USA.Google Scholar
Corpas Pastor, G. (2016). Computerised and Corpus-based Approaches to Phraseology: Monolingual and Multilingual Perspectives (Full papers). Geneva: Tradulex. [[URL]].Google Scholar
Corpas Pastor, G., Colson, J. P. & Heid, U. (Eds.). (forthcoming). Computational Phraseology. Amsterdam & New York: John Benjamins.
Corpas Pastor, G., Monti, J., Seretan, V., & Mitkov, R. (Eds.). (2016). Workshop proceedings: Multi-word units in machine translation and translation technologies (MUMTTT 2015), Malaga, Spain. Geneva: Editions Tradulex.Google Scholar
Corpas Pastor, G. (ed.) (2016). Computerised and Corpus-based Approaches to Phraseology: Monolingual and Multilingual Perspectives (Full papers). Geneva: Tradulex. [[URL]]Google Scholar
Costa-Jussà, M. R., & Farrús, M. (2014). Statistical machine translation enhancements through linguistic levels: A survey. ACM Computing Surveys (CSUR), 46(3), 42. DOI logoGoogle Scholar
Cowie, A. P. 1981. The treatment of collocations and idioms in learners' dictionaries. Applied Linguistics 2 (3), 223–235.DOI logoGoogle Scholar
Dagan, I., & Church, K. (1994). Termight: Identifying and translating technical terminology. In Proceedings of the fourth conference on Applied natural language processing (pp.34–40). Stuttgart, Germany.DOI logoGoogle Scholar
Daille, B. (1994). Approche mixte pour l’extraction automatique de terminologie : statistiques lexicales et filtres linguistiques (Unpublished doctoral dissertation). Université Paris 7.Google Scholar
(2001). Extraction de collocation à partir de textes. Actes de la 8ème conférence sur le Traitement Automatique des Langues Naturelles (TALN’2001). (pp.3–8). Tours, France.Google Scholar
Diab, M. T., & Bhutada, P. (2009). Verb noun construction MWE token supervised classification. In Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (pp.17–22). Suntec, Singapore.Google Scholar
Dowdall, J., Rinaldi, F., Ibekwe-SanJuan, F., & SanJuan, E. (2003). Complex structuring of term variants for Question Answering. In Proceedings of the ACL 2003 workshop on multiword expressions: Analysis, acquisition and treatment (pp.1–8). Sapporo, Japan.DOI logoGoogle Scholar
Evert, S. (2004). The statistics of word cooccurrences: Word pairs and collocations (Unpublished doctoral dissertation). University of Stuttgart.Google Scholar
Fazly, A., Cook, P., & Stevenson, S. (2009). Unsupervised type and token identification of idiomatic expressions. Computational Linguistics, 35(1):61–103. DOI logoGoogle Scholar
Fazly, A. (2007). Automatic acquisition of lexical knowledge about multiword predicates (Unpublished doctoral dissertation). University of Toronto.Google Scholar
Fellbaum, C. (1993). ‘The Determiner in English Idioms’, in C. Cacciari & P. Tabossi (eds) Idioms: Processing, Structure, and Interpretation. Hillsdale, NJ: Erlbaum, 271–295.Google Scholar
(2007). Idioms and collocations: Corpus-based linguistic and lexicographic studies. Bloomsbury Academic.Google Scholar
Fernando, C. & Flavell R. (1981) On Idiom: Critical Views and Perspectives. Exeter Linguistic Studies vol. 5. Exeter: University of Exeter.Google Scholar
Fernández Parra, M. A. (2011). Formulaic Expressions in Computer-Assisted Translation. A specialised translation approach (Unpublished doctoral dissertation). Swansea University.Google Scholar
Finlayson, M., & Kulkarni, N. (2011). Detecting multi-word expressions improves Word Sense Disambiguation. In Proceedings of the workshop on multiword expressions: From parsing and generation to the real world (pp.20–24). Portland, Oregon, UASGoogle Scholar
Firth, J. R. (1957). Papers in Linguistics 1934–1951. London: Oxford University Press.Google Scholar
Fraser, B. 1970. Idioms within a transformational grammar. Foundations of Language 6. 22–42Google Scholar
Franz, A., Horiguchi, K., Duan, L., Ecker, D., Koontz, E., & Uchida, K. (2000). An integrated architecture for example-based machine translation. In Proceedings of the 18th conference on Computational linguistics, Volume 2 (pp.1031–1035). Saarbrücken, Germany DOI logoGoogle Scholar
Gangadharaia, R., & Balakrishanan, N. (2006). Application of linguistic rules to generalized example based Machine Translation for Indian languages. In Proceedings of first National symposium on modeling and shallow parsing of Indian languages (MSPIL). Mumbay, IndiaGoogle Scholar
Geoffrey Leech, R. G., & Bryant, M. (1994). CLAWS4: The tagging of the British National Corpus. In Proceedings of the 15th International Conference on Computational Linguistics (COLING-94) (pp. 622–628). Kyoto, Japan.Google Scholar
(2011). CLAWS4: The tagging of the British National Corpus. In Proceedings of the 15th International Conference on Computational Linguistics (COLING-94) (pp.622–628). Kyoto, Japan.Google Scholar
Gibbs, R. and N. Nayak (1989) “Psycholinguistic Studies on the Syntactic Behavior of Idioms,” Cognitive Psychology 21, 100–138 DOI logoGoogle Scholar
Girju, R., Moldovan, D., Tatu, M., & Antohe, D. (2005). On the semantics of noun compounds. Journal of Computer Speech and Language - Special Issue on Multiword Expressions, 19 (4), 479–496. DOI logoGoogle Scholar
Granger, S., & Meunier, F. (2008). Disentangling the phraseological web. In Granger, S., & Meunier, F. (Eds.), Phraseology. An interdisciplinary perspective. Amsterdam: John Benjamins publishers. DOI logoGoogle Scholar
Grégoire, N., Evert, S., & Krenn, B. (Eds.). (2008). Proceedings of the LREC workshop towards a shared task for multiword expressions (MWE 2008). Marrakech, Morocco.Google Scholar
Groves, D., Hearne, M., & Way, A. (2004). Robust sub-sentential alignment of phrase-structure trees. In Proceedings of the 20th international conference on Computational Linguistics, (pp.1072–1078). Geneva, Switzerland.Google Scholar
Hazelbeck, G., & Saito, H. (2010). A hybrid approach for functional expression identification in a Japanese reading assistant. In Proceedings of the 2010 workshop on multiword expressions: From theory to applications (pp.81–84). Beijing, China.Google Scholar
Huet, S., & Langlais, Ph. (2011). Identifying the translations of idiomatic expressions using TransSearch. In Proceedings of the 8th International NLPCS Workshop (Human-Machine Interaction in Translation (pp.45–56). Copenhagen, Denmark.Google Scholar
(2012). Translation of idiomatic expressions across different languages: A study of the effectiveness of TransSearch. In Neustein, A. & Markowitz, J. A. (Eds.) Where Humans Meet Machines. Innovative Solutions for Knotty Natural-Language Problems (pp.185–209). New York: Springer.Google Scholar
Hurskainen, A. (2008). Multiword expressions and machine translation. Technical Reports in Language Technology, Report No 1.Google Scholar
Jackendoff, R. (1997). The Architecture of the Language Faculty, Cambridge, Mass., MIT Press.Google Scholar
Jian, J. Y., Chang, Y. C., & Chang, J. S. (2004). Collocational translation memory extraction based on statistical and linguistic information. ROCLING 2004, Conference on Computational Linguistics and Speech Processing (pp.329–346). Taipei, Taiwan.Google Scholar
Kalchbrenner, N., & Blunsom, P. (2013). Recurrent convolutional neural networks for discourse compositionality. In Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, (pp.119–126). Sofia, Bulgaria. arXiv preprint arXiv:1306.3584.Google Scholar
Katz, J., & Postal, P. (1963).The semantic interpretation of idioms and sentences containing them. MIT Research Laboratory of Electronic Quarterly Progress Report, 70, 275–282.Google Scholar
Katz, G., & Giesbrecht, E. (2006, July). Automatic identification of non-compositional multi-word expressions using latent semantic analysis. In Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties (pp. 12–19). Association for Computational Linguistics.DOI logoGoogle Scholar
Kilgarriff, Adam, Jakubíček, Miloš, Kovář, Voytěch, Rychlý, P., & Suchomel, V. (2014). Finding terms in corpora for many languages with the Sketch Engine. In Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics (pp.53–56). Gothenburg, Sweden.Google Scholar
Klebanov, B. B., Burstein, J., & Madnani, N. (2013). Sentiment Profiles of multiword expressions in test-taker essays: The case of noun-noun compounds. ACM Transactions for Speech and Language Processing, Special Issue on Multiword Expressions: From Theory to Practice, 10 (3), 12:1–12:15.Google Scholar
Koehn, P., Och, F. J., Marcu, D. (2003). Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1 1, (NAACL '03). (pp.48–54). Edmonton, Canada.Google Scholar
Korkontzelos, I., & Manandhar, S. (2010). Can recognising multiword expressions improve shallow parsing? In Human language technologies: The 2010 annual conference of the North American chapter of the Association for Computational Linguistics (pp.636–644). Los Angeles, California, USA.Google Scholar
Kovář, V., Baisa, V., & Jakubíček, M. (2016). Sketch Engine for bilingual lexicography. International Journal of Lexicography, 29 (3), 339–352. DOI logoGoogle Scholar
Krenn, B. (2000). The usual suspects: Data-oriented models for identification and representation of lexical collocations (Vol. 7). Saarbrücken, Germany: German Research Center for Artificial Intelligence and Saarland University Dissertations in Computational Linguistics and Language Technology.Google Scholar
Lambert, P., & Banchs, R. (2006). Grouping multi-word expressions according to part-of-speech in statistical machine translation. In Proceedings of the EACL Workshop on Multi-word expressions in a multilingual context, (pp.9–16). Trento, Italy.Google Scholar
(2005). Data inferred multi-word expressions for statistical machine translation. In Proceedings of Machine Translation Summit X (pp.396–403). Phuket, Thailand.Google Scholar
Lau, J. H., Baldwin, T., & Newman, D. (2013). On collocations and topic models. ACM Transactions on Speech and Language Processing, 10 (3), 10:1–10:14. DOI logoGoogle Scholar
Lewis, D. D., & Croft, W. B. (1990). Term clustering of syntactic phrases. In Proceedings of 13th international ACM-SIGIR conference on research and development in information retrieval (SIGIR’90) (pp.385–404). Brussels, Belgium.Google Scholar
Lin, D. (1998). Using collocation statistics in information extraction. In Proceedings of the seventh message understanding conference (MUC-7). Fairfax, Virginia, USA.Google Scholar
Luong, M. T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2014). Addressing the rare word problem in neural machine translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, (pp. 11–19). Beijing, China. arXiv preprint arXiv: 1410.8206.Google Scholar
Macken, L. (2009). In search of the recurrent units of translation. In Daelemans, W., & Hoste, V. (Eds.), Evaluation of Translation Technology (pp.195–212). Brussels: Academic and Scientific Publishers.Google Scholar
Makkai, A. 1972. Idiom structure in English (Janua Linguarum, series maior, 48). The Hague: Mouton.DOI logoGoogle Scholar
Mandala, R., Tokunaga, T., & Tanaka, H. (2000). Query expansion using heterogeneous thesauri. Information Processing and Management, 36 (3), 361–378. DOI logoGoogle Scholar
Manrique-Losada, B., Zapata-Jaramillo, C. M., & Burgos, D. A. (2013). Exploring MWEs for knowledge acquisition from corporate technical documents. In Proceedings of the 9th workshop on multiword expressions (pp.82–86). Atlanta, Georgia, USA.
Marcu, D., Wang, W., Echihabi, A., & Knight, K. (2006). SPMT: Statistical machine translation with syntactified target language phrases. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (pp.44–52). Sydney, AustraliaGoogle Scholar
Marvel, A., & Koenig, J.-P. (2015). Event categorization beyond verb senses. In Proceedings of the 11th workshop on multiword expressions (pp.77–86). Denver, Colorado, USA.
Melamed, I. D. (1997, July). A word-to-word model of translational equivalence. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics (pp. 490–497). Association for Computational Linguistics.Google Scholar
Mitkov, R. (Ed.). (forthcoming). The Oxford handbook of computational linguistics. Oxford University Press. DOI logo
(2016). “Computational Phraseology light: automatic translation of multiword expressions without translation resources”. Yearbook of Phraseology, 26(7), 149–166.Google Scholar
Monti, J. (2013). Multi-word unit processing in Machine Translation: developing and using language resources for multi-word unit processing in Machine Translation. (Unpublished doctoral dissertation). University of Salerno, Italy.Google Scholar
Monti, J, Arhan, M. & Sangati F. (forthcoming). Translation asymmetries of Multiword Expressions in Machine Translation: an analysis of the TED-MWE corpus. In Corpas Pastor, G., Colson, J. P. & Heid, U. (Eds.). (forthcoming). Computational Phraseology. Amsterdam & New York: John Benjamins.
Monti, J., Elia, A., Postiglione, A., Monteleone, M., & Marano, F. (2012). In search of knowledge: text mining dedicated to technical translation. In Proceedings of ASLIB 2011 - Translating and the Computer Conference. London, United Kingdom.Google Scholar
Monti, J., Mitkov, R., Seretan V. & Corpas Pastor, G. (Eds.). (2018) Workshop proceedings Multi-word units in Machine Translation and Translation Technology (MUMTTT2017). London, United Kingdom. Geneva: Editions Tradulex.
Monti, J., Mitkov, R., Corpas Pastor, G., & Seretan, V. (Eds.). (2013). Workshop proceedings: Multi-word units in machine translation and translation technologies. Nice, France: The European Association for Machine Translation.Google Scholar
Moon, R. (1998). Fixed expressions and idioms in English: A corpus-based approach. Oxford: Claredon Press Oxford.Google Scholar
(1988). Fixed expressions and idioms in English: A corpus-based approach. (Oxford studies in lexicography and lexicology.) Oxford: Clarendon Press.Google Scholar
Moreno-Ortiz, A., Perez-Hernandez, C., & Del-Olmo, M. (2013). Managing multiword expressions in a lexicon-based sentiment analysis system for Spanish. In Proceedings of the 9th workshop on multiword expressions (pp.1–10). Atlanta, Georgia, USA.Google Scholar
Nivre, J., & Nilsson, J. (2004). Multiword units in syntactic parsing. In MEMURA 2004 – Workshop on Multi-word-expressions in a Multilingual Context held in conjunction with the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006) (pp.39–46). Trento, Italy.Google Scholar
Nagy, I. (2014). Detecting Multiword Expressions and Named Entities in Natural Language Texts, Doctoral dissertation, Ph. D. dissertation, University of Szeged.Google Scholar
Nokel, M., & Loukachevitch, N. (2015). A method of accounting bigrams in topic models. In Proceedings of the 11th workshop on multiword expressions (pp.1–9). Denver, Colorado, USA.DOI logoGoogle Scholar
Nomiyama, H. (1992). Machine translation by case generalization. In Proceedings of the 14th conference on Computational linguistics–Volume 2, (pp.714–720). Nantes, France.DOI logoGoogle Scholar
Nunberg, G., Sag, I.A., Wasow, T. 1994. Idioms. Language 70 (3). 491–538.DOI logoGoogle Scholar
Och, F. J., & Marcu, D. (2003). Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1 (pp.48–54). Edmonton, Canada.Google Scholar
Okita, T., Guerra, M. A., Graham, Y., & Way, A. (2010). Multi-word expression-sensitive word alignment. In Proceedings of the 4th International Workshop on Cross Lingual Information Access at COLING 2010 (pp.26–34). Beijing, China.Google Scholar
Okuma, H., Yamamoto, H., & Sumita, E. (2008). Introducing a translation dictionary into phrase-based SMT. IEICE transactions on information and systems, 91(7), 2051–2057. DOI logoGoogle Scholar
Orlandi, A. & Giacomini, L. (eds.) 2016. Defining collocations for lexicographic purposes: from linguistic theory to lexicographic practice (Series ‘Linguistic Insights’). Frankfurt: Peter Lang.DOI logoGoogle Scholar
Ozdowska, S. (2006). ALIBI, un systeme d’ALIgnement BIlingue base de regles (Doctoral dissertation PhD thesis), Université de Toulouse 2.Google Scholar
Pal, S., Chakraborty, T., & Bandyopadhyay, S. (2011). Handling multiword expressions in phrase-based statistical machine translation. Machine Translation Summit XIII, (pp.215–224). Xiamen, China.Google Scholar
Pal, S., Kumar Naskar, S., Pecina, P., Bandyopadhyay, S., & Way, A. (2010). Handling named entities and compound verbs in phrase-based statistical machine translation. In Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications (pp.46–54). Beijing, ChinaGoogle Scholar
Pawley, A. & Syder, F. H. (1983). Two puzzles for linguistic theory: Native like selection and native like fluency. In J. J. Richards, & R. R. W. Schmidt (eds.), Language and Communication (pp.191–225). Harlow: Longman.Google Scholar
Pearce, D. (2002). A Comparative Evaluation of Collocation Extraction Techniques. In Proceedings of Ninth International Conference on Language Resources and Evaluation (LREC2002) (pp. 1530–1536). Las Palmas, Spain.Google Scholar
Pecina, P. (2008). Lexical association measures: Collocation extraction (Unpublished doctoral dissertation). Charles University.Google Scholar
Ramisch, C. (2012). A generic and open framework for multiword expressions treatment: from acquisition to applications (Unpublished doctoral dissertation). University of Grenoble and Federal University of Rio Grande do Sul.Google Scholar
(2015). Multiword expressions acquisition: A generic and open framework (Vol. XIV). Springer.Google Scholar
Ramisch, C., Villavicencio, A. (forthcoming) Computational treatment of multiword expressions. In Mitkov, R. (Ed.). (forthcoming). The Oxford handbook of computational linguistics. Oxford University Press.
Ramisch, C., Villavicencio, A., & Kordoni, V. (2013). Introduction to the special issue on multiword expressions: From theory to practice and use. ACM Transactions on Speech and Language Processing, 10 (2), 3:1–3:10. (Special issue on Multiword Expressions. DOI logoGoogle Scholar
Rapp, R., Sharoff, S. (2014). Extracting multiword translations from aligned comparable documents. Proceedings of the 3rd Workshop on Hybrid Approaches to Translation (HyTra) (pp.87–95). Gothenburg, SwedenGoogle Scholar
Rayson, P., Piao, S., Sharoff, S., Evert, S., & Moirón, B. V. (2010). Multiword expressions: hard going or plain sailing? Language Resources and Evaluation Special Issue on Multiword expressions: Hard going or plain sailing, 44 (1–2), 1–25. (Special issue on Multiword Expressions)Google Scholar
Ren, Z., Lü, Y., Cao, J., Liu, Q., & Huang, Y. (2009). Improving statistical machine translation using domain bilingual multiword expressions. In Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (pp.47–54). Suntec, Singapore.Google Scholar
Rikters M., & Bojar O. (2017). Paying Attention to Multi-Word Expressions in Neural Machine Translation. In MT Summit XVI Proceedings Nagoya, Japan, September 18–22, 2017, vol. 1: Research Track, (pp. 86–95). Nagoya, Japan.Google Scholar
Riloff, E. (2005). Little words can make a big difference for text classification. In Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval (pp.130–136). Seattle, Washington, USA.Google Scholar
Rohanian, O., Taslimipoor, S., Yaneva, V. and L. A. Ha (2017). Using Gaze Data to Predict Multiword Expressions. In Proceedings of the 11th Conference on Advances in Natural Language Processing (RANLP 2017), Varna, Bulgaria.DOI logoGoogle Scholar
Sag, I. A., Baldwin, T., Bond, F., Copestake, A., & Flickinger, D. (2002). Multiword expressions: A pain in the neck for NLP. In Proceedings of the third international conference on intelligent text processing and computational linguistics (CICLING 2002) (pp.1–15). Mexico City, Mexico.DOI logoGoogle Scholar
Salehi, B. Mathur, N., Cook, P. & Baldwin, T. (2015). The impact of multiword expression compositionality on machine translation evaluation. In Proceedings of the 11th Workshop on MWEs (MWE 2015) (pp.54–59). Denver, Colorado, USA.Google Scholar
Salton, G., & Smith, M. (1989). On the application of syntactic methodologies in automatic text analysis. In Proceedings of the 12th annual international ACM SIGIR conference on research and development in information retrieval (pp.137–150). New York, USA.Google Scholar
Sanjuan, E., Dowdall, J., Ibekwe-Sanjuan, F., & Rinaldi, F. (2005). A symbolic approach to automatic multiword term structuring. Journal of Computer Speech and Language – Special Issue on Multiword Expressions, 19 (4), 524–542.Google Scholar
Savary, A., Ramisch, C., Cordeiro, S., Sangati, F., Vincze, V., Qasemi Zadeh, B., Candito, M., Cap, F., Giouli, V., Stoyanova, I & Doucet, A. (2017). The PARSEME shared task on automatic identification of verbal multiword expressions. In Proceedings of the 13th workshop on multiword expressions (MWE 2017) (pp.31–47). Valencia, Spain.
Schneider, N. (2014). Lexical Semantic Analysis in Natural Language Text. Doctoral dissertation, Ph. D. dissertation, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. Carnegie Mellon University.Google Scholar
Schneider, N., Onuffer, S., Kazour, N., Danchik, E., Mordowanec, M. T., Conrad, H., and Smith, N. A. (2014). Comprehensive annotation of multiword expressions in a social web corpus. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’14) (pp.455–461). Reykjavik, Island.Google Scholar
Schneider, N., Hovy, D., Johannsen, A., & Carpuat, M. (2016). Semeval-2016 task 10: Detecting minimal semantic units and their meanings (dimsum). In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (pp. 546–559).Google Scholar
Scott, B. (2003). The Logos model: An historical perspective. Machine Translation, 18 (1), 1–72. DOI logoGoogle Scholar
Scott, B., & Barreiro, A. (2009). OpenLogos MT and the SAL representation language. In Proceedings of the First International Workshop on Free/Open-Source Rule-Based Machine Translation (pp.19–26). Alacant, Spain.Google Scholar
Segura, J., & Prince, V. (2011). Using Alignment to detect associated multiword expressions in bilingual corpora. Tralogy. Paris, France.Google Scholar
Seretan, V. (2008). Collocation extraction based on syntactic parsing (Unpublished doctoral dissertation). University of Geneva.Google Scholar
(2009). Extraction de collocations et leurs équivalents de traduction à partir de corpus parallèles. TAL, 50(1), 305–332.Google Scholar
(2011). A collocation-driven approach to text summarization. In Actes de la 18e conférence sur le traitement automatique des langues naturelles (TALN 2011) (pp.9–14). Montpellier, France.Google Scholar
(2011). Syntax-based collocation extraction (Vol. 44). Dordrecht: Springer. DOI logoGoogle Scholar
Seretan, V., & Wehrli, E. (2007). Collocation translation based on sentence alignment and parsing. In Proceedings of Traitement Automatique des Langues Naturelles (TALN) (pp.401–410). Toulouse, France.
Shigeto, Y., Azuma, A., Hisamoto, S., Kondo, S., Kouse, T., Sakaguchi, K., Yoshimoto, A., Yung, F. & Matsumoto, Y. (2013). Construction of English MWE dictionary and its application to POS tagging. In Proceedings of the 9th workshop on multiword expressions (pp.139–144). Atlanta, Georgia, USA.Google Scholar
Sinclair, J. (1991). Corpus, concordance, collocation. Oxford: Oxford University Press.Google Scholar
Sinclair, J. McH. (1996). The search for units of meaning. Textus, 9(1), 75–106.Google Scholar
Sinclair, J. M. (2007). Collocation reviewed. (manuscript), Tuscan Word Centre, Italy.
Sinclair, J. (2008). Preface. In Granger, S., & Meunier, F. (Eds.), Phraseology. An interdisciplinary perspective. Amsterdam: John Benjamins publishers.DOI logoGoogle Scholar
Smadja, F. (1993). Retrieving collocations from text: Xtract. Computational linguistics, 19(1), 143–177.Google Scholar
Straňák, P. (2010). Annotation of multiword expressions in the Prague Dependency Treebank (Unpublished doctoral dissertation). Charles University.Google Scholar
Sumita, E., & Iida, H. (1991). Experiments and prospects of example-based machine translation. In Proceedings of the 29th annual meeting on Association for Computational Linguistics (pp.185–192). Berkeley, California DOI logoGoogle Scholar
Sumita, E., Iida, H., & Kohyama, H. (1990). Translating with examples: a new approach to machine translation. The Third International Conference on Theoretical and Methodological Issues in Machine Translation of Natural Language (pp.203–212) Austin, Texas, USA.Google Scholar
Tambouratzis, G., Troullinos, M., Sofianopoulos, S., & Vassiliou, M. (2012). Accurate phrase alignment in a bilingual corpus for EBMT systems. In Proceedings of the 5th BUCC Workshop, held within the International Conference on Language Resources and Evaluation (LREC2012) , Vol. 26, (pp.104–111). Istanbul, Turkey.
Tang, Y., Meng, F., Lu, Z., Li, H., & Yu, P. L. (2016). Neural machine translation with external phrase memory. arXiv preprint arXiv:1606.01792.Google Scholar
Taslimipoor, S., Rohanian, O., Mitkov, R. & A. Fazly. (2017). Investigating the opacity of verb-noun multiword expression usages in context. In Proceedings of the 13th Workshop on Multiword Expressions, MWE@EACL 2017, Valencia, Spain, April 4, 133–138.
Taslimipoor, S., Mitkov, R., Mitkov, R. & A. Fazly. (2016). “Bilingual Contexts from Comparable Corpora to Mine for Translations of Collocations”. In Proceedings of the 17thInternational Conference on Intelligent Text Processing and Computational Linguistics (CICLing2016), Konya, Turkey.Google Scholar
Taslimipoor, S. (2015). “Cross-lingual Extraction of Multiword Expressions”. In Corpas Pastor, G.(ed.) (2016). Computerised and Corpus-based Approaches to Phraseology: Monolingual and Multilingual Perspectives (Full papers), Geneva: Tradulex. [[URL]]Google Scholar
Thurmair, G. (2004). Multilingual Content Processing. In Proceedings of the 4th International Conference on Language Resources and Evaluation (LRE2004), (pp.XI–XVI). Lisbon, Portugal.Google Scholar
Tillmann, C., & Xia, F. (2003). A phrase-based unigram model for statistical machine translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003–short papers, (pp.106–108). Edmonton, CanadaGoogle Scholar
Tomokiyo, T., & Hurst, M. (2003). A language model approach to keyphrase extraction. In Proceedings of the ACL 2003 workshop on multiword expressions: Analysis, acquisition and treatment (pp.33–40). Sapporo, Japan DOI logoGoogle Scholar
Tsvetkov, Y. (2010). Extraction of multi-word expressions from small parallel corpora (Unpublished doctoral dissertation). University of Haifa.Google Scholar
Ullman, E., & Nivre, J. (2014). Paraphrasing Swedish compound nouns in Machine Translation. In Proceedings of the 10th workshop on multiword expressions (MWE) (pp.99–103). Gothenburg, Sweden.Google Scholar
Váradi, T. (2006). Multiword Units in an MT Lexicon. In Proceedings of the EACL Workshop on Multi-Word Expressions in a Multilingual Contexts, (pp.73–78). Trento, Italy.Google Scholar
Venkatapathy, S., & Joshi, A. K. (2006). Using information about multi-word expressions for the word-alignment task. In Proceedings of the workshop on multiword expressions: Identifying and exploiting underlying properties (pp.20–27). Sydney, Australia.DOI logoGoogle Scholar
Venkatsubramanyan, S., & Perez-Carballo, J. (2004). Multiword expression filtering for building knowledge. In T. Tanaka, A. Villavicencio, F. Bond, & A. Korhonen (Eds.), Second ACL workshop on multiword expressions: Integrating processing. (pp.40–47) Barcelona, Spain. DOI logoGoogle Scholar
Villavicencio, A., Bond, F., Korhonen, A., & McCarthy, D. (2005). Introduction to the special issue on multiword expressions: Having a crack at a hard nut. Computer Speech & Language, 19 (4), 365–377. (Special issue on Multiword Expressions. DOI logoGoogle Scholar
Villavicencio, A., Kordoni, V., Zhang, Y., Idiart, M., & Ramisch, C. (2007). Validation and evaluation of automatically acquired multiword expressions for grammar engineering. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CONLL) (pp.1034–1043). Prague, Czech Republic.Google Scholar
Vintar, S., & Fiser, D. (2008). Harvesting Multi-Word Expressions from Parallel Corpora. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08) (pp.1091–1096). Marrakech, Morocco.Google Scholar
Wacholder, N., & Song, P. (2005). Toward a task-based gold standard for evaluation of NP chunks and technical terms. In Proceedings of the 2003 Human Language Technology conference of the North American Chapter of the Association for Computational Linguistics (pp.130–136). Edmonton, CanadaGoogle Scholar
Wang, L., & Yu, S. (2010). Construction of Chinese idiom knowledge-base and its applications. In Proceedings of the 2010 workshop on multiword expressions: From theory to applications (pp.11–18). Beijing, China.Google Scholar
Wehrli, E. (2014). The relevance of collocations for parsing. In Proceedings of the 10th workshop on multiword expressions (MWE 2014) (pp.26–32). Gothenburg, Sweden.Google Scholar
Wehrli, E., Seretan, V., & Nerima, L. (2010). Sentence analysis and collocation identification. In Proceedings of the workshop on multiword expressions: from theory to applications (MWE 2010) (pp.27–35). Beijing, China.Google Scholar
Widdows, D., & Dorow, B. (2005, June). Automatic extraction of idioms using graph analysis and asymmetric lexicosyntactic patterns. In Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition (pp. 48–56). Association for Computational Linguistics.DOI logoGoogle Scholar
Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A., & Spasić, I. (2015). The role of idioms in sentiment analysis. Expert Syst. Appl., 42 (21), 7375–7385.DOI logoGoogle Scholar
Wu, C. C., & Chang, J. S. (2004). Bilingual Collocation Extraction Based on Syntactic and Statistical Analyses. Computational Linguistics and Chinese Language Processing, 9(1):1–20.Google Scholar
Wu, H., Wang, H., & Zong, C. (2008). Domain adaptation for statistical machine translation with domain dictionary and monolingual corpora. In Proceedings of the 22nd International Conference on Computational Linguistics–Volume 1 (pp.993–1000), Manchester, United Kingdom.Google Scholar
Yaneva, V., Taslimipoor, S., Rohanian, O. & L. A. Ha. (2017). Cognitive Processing of Multiword Expressions in Native and Non-native Speakers of English: Evidence from Gaze Data”. In Mitkov, R. (Ed.) Computational and Corpus-based Phraseology. Springer: Heidelberg, New York, London.DOI logoGoogle Scholar
Yarowsky, D. (1993). One sense per collocation. In Proceedings of ARPA Human Language Technology workshop (pp.266–271). Princeton, New Jersey, USA. DOI logoGoogle Scholar
(1995). Unsupervised word sense disambiguation rivalling supervised methods. In Proceedings of the 33rd annual meeting of the Association for Computational Linguistics (ACL 1995) (pp.189–196). Cambridge, Massachusetts, USA.DOI logoGoogle Scholar
Zens, R., Och, F. J., & Ney, H. (2002). Phrase-based statistical machine translation. Annual Conference on Artificial Intelligence (pp.18–32). Edmonton, Canada.Google Scholar
Zhang, Y., & Kordoni, V. (2006). Automated deep lexical acquisition for robust open texts processing. In Proceedings of 5th International Conference on Language Resources and Evaluation (LRE2006)–2006 (pp.275–280). Genoa, Italy.Google Scholar
Zollmann, A., & Venugopal, A. (2006). Syntax augmented machine translation via chart parsing. In Proceedings of the Workshop on Statistical Machine Translation (pp.138–141). New York city, USA.DOI logoGoogle Scholar
Cited by (12)

Cited by 12 other publications

Kübler, Natalie, Hanna Martikainen, Alexandra Mestivier & Mojca Pecman
2024. Chapter 4. Post-editing neural machine translation in specialised languages. In Recent Advances in Multiword Units in Machine Translation and Translation Technology [Current Issues in Linguistic Theory, 366], ► pp. 57 ff. DOI logo
Martín Gascón, Beatriz
2024. Chapter 11. Irony in American-English tweets. In Recent Advances in Multiword Units in Machine Translation and Translation Technology [Current Issues in Linguistic Theory, 366], ► pp. 197 ff. DOI logo
Speranza, Giulia & Johanna Monti
2024. Chapter 3. Evaluating the Italian-English machine translation quality of MWUs in the domain of archaeology. In Recent Advances in Multiword Units in Machine Translation and Translation Technology [Current Issues in Linguistic Theory, 366], ► pp. 40 ff. DOI logo
Corpas Pastor, Gloria & Enrique Gutiérrez Rubio
2023. Computational and corpus phraseology applied to Spanish. Romanica Olomucensia 35:1 ► pp. 1 ff. DOI logo
Hidalgo-Ternero, Carlos Manuel & Xiaoqing Zhou-Lian
2022. Reassessing gApp: Does MWE Discontinuity Always Pose a Challenge to Neural Machine Translation?. In Computational and Corpus-Based Phraseology [Lecture Notes in Computer Science, 13528], ► pp. 116 ff. DOI logo
Giczela-Pastwa, Justyna
2021. Developing phraseological competence in L2 legal translator trainees: a proposal of a data mining technique applied in translation from an LLD into ELF. The Interpreter and Translator Trainer 15:2 ► pp. 187 ff. DOI logo
Stasimioti, Maria, Vilelmini Sosoni & Konstantinos Chatzitheodorou
2021. Investigating post-editing effort. Cognitive Linguistic Studies 8:2 ► pp. 378 ff. DOI logo
Corpas Pastor, Gloria & Jean-Pierre Colson
2020. Introduction. In Computational Phraseology [IVITRA Research in Linguistics and Literature, 24], ► pp. 2 ff. DOI logo
Hidalgo-Ternero, Carlos Manuel & Gloria Corpas Pastor
2020.  Bridging the “gApp”: improving neural machine translation systems for multiword expression detection . Yearbook of Phraseology 11:1 ► pp. 61 ff. DOI logo
Monti, Johanna & Maria Pia di Buono
2019. PARSEME-It: an Italian corpus annotated with verbal multiword expressions. Italian Journal of Computational Linguistics 5:2 ► pp. 61 ff. DOI logo
Seretan, Violeta
2018. Bridging Collocational and Syntactic Analysis. In Lexical Collocation Analysis [Quantitative Methods in the Humanities and Social Sciences, ], ► pp. 23 ff. DOI logo
Ramisch, Carlos
2017. Putting the Horses Before the Cart: Identifying Multiword Expressions Before Translation. In Computational and Corpus-Based Phraseology [Lecture Notes in Computer Science, 10596], ► pp. 69 ff. DOI logo

This list is based on CrossRef data as of 13 november 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.