Part of
Computational Phraseology
Edited by Gloria Corpas Pastor and Jean-Pierre Colson
[IVITRA Research in Linguistics and Literature 24] 2020
► pp. 111134
References
Baldwin, T., & Kim, S. N.
(2010) Multiword expressions. In N. Indurkhya and F. J. Damerau (Eds.), Handbook of Natural Language Processing. 2 edition (pp. 267–292). Boca Raton, FL: CRC Press, Taylor and Francis Group.Google Scholar
Banerjee, S., & Pedersen, T.
(2003) The design, implementation, and use of the Ngram Statistic Package. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics (370–381). Mexico City, Mexico. DOI logoGoogle Scholar
Baroni, M., & Bernardini, S.
(Eds.) (2006) Wacky! Working papers on the Web as Corpus. Bologna, Italy: GEDIT.Google Scholar
Bond, F., Kim, S. N., Nakov, P., & Szpakowicz, S.
(Eds) (2013) Journal of Natural Language Engineering.Special Issue on computational approaches to the semantics of noun compounds, 19(3). Cambridge, UK:Cambridge University Press.Google Scholar
Bonin, F., Dell’Orletta, F., Montemagni, S., & Venturi, G.
(2010) A contrastive approach to multiword extraction from domain-specific corpora. In Proceeginds of the Seventh LREC (LREC 2010), Valetta, Malta: ELRA.Google Scholar
Carpuat, M., & Diab, M.
(2010) Task-based evaluation of multiword expressions: a pilot study in statistical machine translation. In Proceedings of HLT: The 2010 Annual Conference of the NAACL (NAACL 2003) (pp. 242–245). Los Angeles, CA: ACL.Google Scholar
Church, K., & Hanks, P.
(1990) Word association norms mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.Google Scholar
Constant, M., Roux, J. L., & Sigogne, A.
(2013) Combining compound recognition and PCFG-LA parsing with word lattices and conditional random fields. ACM Transactions on Speech and Language Processing. Special Issue on MWEs: from theory to practice and use, part 2 (TSLP), 10(3).Google Scholar
Constant, M., & Tellier, I.
(2012) Evaluating the impact of external lexical resources into a CRF-based multiword segmenter and part-of-speech tagger. In Proceedings of the Eigth LREC (LREC 2012), Istanbul, Turkey: ELRA.Google Scholar
Cook, P., & Stevenson, S.
(2010) Automatically identifying the source words of lexical blends in English. Computational Linguistics, 36(1), 129–149. DOI logoGoogle Scholar
Cordeiro, S., Ramisch, C., Idiart, M., & Villavicencio, A.
(2016a) Predicting the compositionality of nominal compounds: Giving word embeddings a hard time. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1986–1997). Association for Computational Linguistics. DOI logoGoogle Scholar
Cordeiro, S., Ramisch, C., & Villavicencio, A.
(2016b) Mwetoolkit+sem: Integrating word embeddings in the mwetoolkit for semantic mwe processing. In LREC 2016 Portoroz, Slovenia.Google Scholar
Dagan, I., & Church, K.
(1994) Termight: Identifying and translating technical terminology. In Proceedings of the 4th ANLP Conference (ANLP 1994) (pp. 34–40). Stuttgart, Germany: ACL.Google Scholar
Daille, B.
(1995) Repérage et extraction de terminologie par une approche mixte statistique et linguistique. Traitement Automatique des Langues, 36(1–2), 101–118.Google Scholar
de Medeiros Caseli, H., Villavicencio, A., Machado, A., & Finatto, M. J.
(2009) Statisticallydriven alignment-based multiword expression identification for technical domains. In D. Anastasiou, C. Hashimoto, P. Nakov, S. N. Kim (Eds.), Proceedings of the ACL Workshop on MWEs: Identification, Interpretation, Disambiguation, Applications (MWE 2009) (pp. 1–8). Suntec, Singapore: ACL.Google Scholar
Drouin, P.
(2004) Detection of domain specific terminology using corpora comparison. In Proceedings of the Fourth LREC (LREC 2004). Lisbon, Portugal: ELRA.Google Scholar
Dunning, T.
(1993) Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.Google Scholar
Duran, M. S., & Ramisch, C.
(2011) How do you feel? Investigating lexical-syntactic patterns in sentiment expression. In Proceedings of Corpus Linguistics 2011: Discourse and Corpus Linguistics Conference. Birmingham, UK.Google Scholar
Duran, M. S., Ramisch, C., Aluísio, S. M., & Villavicencio, A.
(2011) Identifying and analyzing Brazilian Portuguese complex predicates. In V. Kordoni, C. Rasmich, & A. Villavicencio (Eds.), Proceedings of the ALC Workshop on MWEs: from Parsing and Generation to the Real World (MWE 2011) (pp. 74–82). Portland, OR: ACL.Google Scholar
Duran, M. S., Scarton, C. E., Aluísio, S. M., & Ramisch, C.
(2013) Identifying pronominal verbs: Towards automatic disambiguation of the clitic ’se’ in Portuguese. In V. Kordoni, C. Rasmich, & A. Villavicencio (Eds.), Proceedings of the 9th Workshop on MWEs (MWE 2013) (pp. 93–100). Atlanta, GA: ACL.Google Scholar
Evert, S.
(2004) The Statistics of Word Cooccurrences: Word Pairs and Collocations. (PhD Thesis, Institut für maschinelle Sprachverarbeitung, University of Stuttgart, Stuttgart, Germany).Google Scholar
Evert, S., & Krenn, B.
(2005) Using small random samples for the manual evaluation of statistical association measures. Computer Speech & Language. Special issue on Multiword Expression, 19(4), 450–466. DOI logoGoogle 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
Finlayson, M., & Kulkarni, N.
(2011) Detecting multi-word expressions improves word sense disambiguation. In V. Kordoni, C. Rasmich, & A. Villavicencio (Eds.), Proceedings of the ALC Workshop on MWEs: from Parsing and Generation to the Real World (MWE 2011) (pp. 20–24). Portland, OR: ACL.Google Scholar
Ha, L. A., Fernandez, G., Mitkov, R., & Corpas Pastor, G.
(2008) Mutual bilingual terminology extraction. In Proceedings of the Sixth LREC (LREC 2008), Marrakech, Morocco: ELRA.Google Scholar
Heid, U.
(2008) Computational phraseology: An overview. In S. Granger, & F. Meunier. (Eds.), Phraseology. An interdisciplinary Perspective (pp. 337-360). Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Heid, U., Fritzinger, F., Hinrichs, E., Hinrichs, M., & Zastrow, T.
(2010) Term and collocation extraction by means of complex linguistic web services. In Proceedings of the Seventh LREC (LREC 2010), Valetta, Malta: ELRA.Google Scholar
Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Rychlý, P., & Suchomel, V.
(2014) The sketch engine: ten years on. Lexicography, 1(1), 7–36. DOI logoGoogle Scholar
Köper, M., Schulte im Walde, S., Kisselew, M., & Padó, S.
(2016) Improving zero-shot-learning for German particle verbs by using training-space restrictions and local scaling. In Proceedings of *SEM 2016 (pp. 91–96). ACL.Google Scholar
Linardaki, E., Ramisch, C., Villavicencio, A., & Fotopoulou, A.
(2010) Towards the construction of language resources for Greek multiword expressions: Extraction and evaluation. In S. Piperidis, M. Slavcheva, & C. Vertan (Eds.), Proceedings of the LREC Workshop on Exploitation of multilingual resources and tools for Central and (South) Eastern European Languages (pp. 31–40). Valetta, Malta.Google Scholar
Manning, C. D., & Schütze, H.
(1999) Foundations of statistical natural language processing. Cambridge, MA: MIT Press.Google Scholar
Markantonatou, S., Ramisch, C., Savary, A., & Vincze, V.
(Eds.) (2017) Proceedings of the 13th Workshop on MWEs (MWE 2017), Valencia, Spain: ACL.Google Scholar
Martens, S. & Vandeghinste, V.
(2010) An efficient, generic approach to extracting multiword expressions from dependency trees. In É. Laporte., P. Nakov, C. Ramisch, & A. Villavicencio (Eds.), Proc. of the COLING Workshop on MWEs: from Theory to Applications (MWE 2010), (pp. 84–87). Beijing, China: ACL.Google Scholar
McKeown, K. R., & Radev, D. R.
(1999) Collocations. In R. Dale, H. Moisl, & H. Somers (Eds.), A Handbook of Natural Language Processing (pp. 507–553). New York, NY: Marcel Dekker.Google Scholar
Mikolov, T., Chen, K., Corrado, G., & Dean, J.
(2013) Efficient estimation of word representations in vector space.CoRR, abs/1301.3781.Google Scholar
Morin, E., & Daille, B.
(2010) Compositionality and lexical alignment of multi-word terms. Language Resources and Evaluation. Special Issue on Multiword expression: hard going or plain sailing, 44(1–2), 79–95. DOI logoGoogle Scholar
Morin, E., Daille, B., Takeuchi, K., & Kageura, K.
(2007) Bilingual terminology mining– using brain, not brawn comparable corpora. In Proceedings of the 45th ACL (ACL 2007) (pp 664–671). Prague, Czech Republic: ACL.Google Scholar
Nakov, P., & Hearst, M. A.
(2005) Search engine statistics beyond the n-gram: Application to noun compound bracketing. In I. Dagan, & D. Gildea (Eds.), Proceeginds of the Ninth CoNLL (CoNLL-2005) (pp. 17-24). University of Michigan, MI: ACL.Google Scholar
Pearce, D.
(2001) Synonymy in collocation extraction. In WordNet and Other Lexical Resources: Applications, Extensions and Customizations (NAACL 2001 Workshop) (pp. 41–46).Google Scholar
Pecina, P.
(2008) Lexical Association Measures: Collocation Extraction. (PhD Thesis, Faculty of Mathematics and Physics, Charles University).Google Scholar
(Rev.) (2011) Syntax-based collocation extraction by Violeta seretan (University of Geneva). Berlin: Springer (Text, Speech and Language Technology Series, volume 44). Computational Linguistics, 37(3), 631–633. DOI logoGoogle Scholar
Pedersen, T., Banerjee, S., McInnes, B., Kohli, S., Joshi, M., & Liu, Y.
(2011) The n-gram statistics package (text: NSP) : A flexible tool for identifying n-grams, collocations, and word associations. In V. Kordoni, C. Rasmich, & A. Villavicencio (Eds.), Proceedings of the ALC Workshop on MWEs: from Parsing and Generation to the Real World (MWE 2011) (pp. 131–133). Portland, OR: ACL.Google Scholar
Ramisch, C.
(2015) Multiword Expressions Acquisition: A Generic and Open Framework, volume XIV of Theory and Applications of Natural Language Processing. Springer. DOI logoGoogle Scholar
Ramisch, C., Araujo, V. D., & Villavicencio, A.
(2012) A broad evaluation of techniques for automatic acquisition of multiword expressions. In Proceedings of the ACL 2012 SRW (pp. 1–6). Jeju, Republic of Korea: ACL.Google Scholar
Ramisch, C., Schreiner, P., Idiart, M., & Villavicencio, A.
(2008) An evaluation of methods for the extraction of multiword expressions. In N. Grégoire, S. Evert, & B. Krenn (Eds.), Proceedings of the LREC Workshop Towards a Shared Task for MWEs (MWE 2008) (pp. 50–53). Marrakech, Morocco.Google Scholar
Ramisch, C., Villavicencio, A., & Boitet, C.
(2010a) Multiword expressions in the wild? The mwetoolkit comes in handy. In Y. Liu, & T. Liu (Eds.), Proceedings of the 23rd COLING (COLING 2010) – Demonstrations (pp. 57–60). Beijing, China: The Coling 2010 Organizing Committee.Google Scholar
(2010b) mwetoolkit: a Framework for Multiword Expression Identification. In Proceeginds of the Seventh LREC (LREC 2010) (pp. 662–669). Valetta, Malta: ELRA.Google Scholar
(2010c) Web-based and combined language models: a case study on noun compound identification. In C.-R. Huang, & D. Jurafsky (Eds.), Proceedings of the 23rd COLING (COLING 2010) – Posters (pp. 1041–1049). Beijing, China: The Coling 2010 Organizing Committee.Google Scholar
Ramisch, C., Villavicencio, A., & Kordoni, V.
(Eds.) (2013) ACM Transactions on Speech and Language Processing. Special Issue on MWEs: from theory to practice and use, part 1 (TSLP), 10(2). New York, NY: ACMGoogle Scholar
Rayson, P., Piao, S., Sharoff, S., Evert, S., & Moirón, B. V.
(Eds.) (2010) Language Resources and Evaluation. Special Issue on Multiword expression: hard going or plain sailing, 44(1–2). Springer.Google Scholar
Riedl, M., & Biemann, C.
(2013) Scaling to large data: An efficient and effective method to compute distributional thesauri. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 884–890). Association for Computational Linguistics.Google Scholar
(2015) A single word is not enough: Ranking multiword expressions using distributional semantics. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 2430–2440). Association for Computational Linguistics. DOI logoGoogle Scholar
Rivera, O. M., Mitkov, R., & Corpas Pastor, G.
(2013) A flexible framework for collocation retrieval and translation from parallel and comparable corpora. In R. Mitkov, J. Monti, G. Corpas Pastor, & V. Seretan (Eds.), Proceedings of the MT Summit 2013 MUMTTT workshop (MUMTTT 2013) (pp. 18–25). Nice, France.Google Scholar
Roller, S., im Walde, S. S., & Scheible, S.
(2013) The (un)expected effects of applying standard cleansing models to human ratings on compositionality. In V. Kordoni, C. Rasmich, & A. Villavicencio (Eds.), Proceedings of the 9th Workshop on MWEs (MWE 2013) (pp. 32–41). Atlanta, GA: ACL.Google Scholar
Sag, I., Baldwin, T., Bond, F., Copestake, A., & Flickinger, D.
(2002) Multiword expressions: A pain in the neck for NLP. In Proceedings of the 3rd CICLing (CICLing-2002), volume 2276/2010 of LNCS (pp. 1–15). Mexico City, Mexico: Springer.Google Scholar
Salehi, B., & Cook, P.
(2013) Predicting the compositionality of multiword expressions using translations in multiple languages. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity (pp. 266–275). Association for Computational Linguistics.Google Scholar
Salehi, B., Cook, P., & Baldwin, T.
(2015) A word embedding approach to predicting the compositionality of multiword expressions. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 977–983). Association for Computational Linguistics.Google Scholar
Sangati, F., Zuidema, W., & Bod, R.
(2010) Efficiently extract rrecurring tree fragments from large treebanks. In Proc. of the Seventh LREC (LREC 2010). Valetta, Malta: ELRA.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: ACL. DOI logoGoogle Scholar
Schneider, N., Onuffer, S., Kazour, N., Danchik, E., Mordowanec, M. T., Conrad, H., & Smith, N. A.
(2014) Comprehensive annotation of multiword expressions in a social web corpus. In Proceedings of the Ninth LREC (LREC 2014). Reykjavik, Iceland: ELRA.Google Scholar
Seretan, V.
(2011) Syntax-Based Collocation Extraction, volume 44 of Text, Speech and Language Technology. 1st edition. Dordrecht, Netherlands: Springer. DOI logo.Google Scholar
Seretan, V., & Wehrli, E.
(2009) Multilingual collocation extraction with a syntactic parser. Language Resources and Evaluation. Special Issue on Multilingual Language Resources and Interoperability, 43(1), 71–85.Google Scholar
Silva, J. & Lopes, G.
(1999) A local maxima method and a fair dispersion normalization for extracting multi-word units from corpora. In Proceedings of the Sixth Meeting on Mathematics of Language (MOL6) (pp. 369–381). Orlando, FL.Google Scholar
Smadja, F. A.
(1993) Retrieving collocations from text: Xtract. Computational Linguistics, 19(1), 143–177.Google Scholar
Tsvetkov, Y., & Wintner, S.
(2011) Identification of multi-word expressions by combining multiple linguistic information sources. In R. Barzilay, M. Johnson (Eds.), Proceedings of the 2011 EMNLP (EMNLP 2011) (pp. 836–845). Edinburgh, Scotland, UK: ACL.Google Scholar
Vargas, N., Ramisch, C., & Caseli, H.
(2017) Discovering light verb constructions and their translations from parallel corpora without word alignment. In Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017) (pp. 91–96). Valencia, Spain: ACL. DOI logoGoogle Scholar
Villavicencio, A., Bond, F., Korhonen, A., & McCarthy, D.
(Eds.) (2005) Computer Speech & Language. Special issue on Multiword Expression, 19(4). Elsevier.Google Scholar
Villavicencio, A., Kordoni, V., Zhang, Y., Idiart, M., & Ramisch, C.
(2007) Validation and evaluation of automatically acquired multiword expressions for grammar engineering. In J. Eisner (Ed.), Proceedings of the 2007 Joint Conference on EMNLP and Computational NLL (EMNLPCoNLL 2007) (pp. 1034–1043). Prague, Czech Republic: ACL.Google Scholar
Weller, M., & Heid, U.
(2012) Analyzing and aligning German compound nouns. In Proceedings of the Eighth LREC (LREC 2012). Istanbul, Turkey: ELRA.Google Scholar
Zhou, X., Zhang, X., & Hu, X.
(2007) Dragon toolkit: Incorporating auto-learned semantic knowledge into large-scale text retrieval and mining. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence– ICTAI 2007, volume 2 (pp. 197–201). Washington, DC: IEEE Computer Society. DOI logoGoogle Scholar
Cited by

Cited by 1 other publications

Lima Florido, Francisco Javier
2023. Computational and Corpus-Based Phraseology. TRANS: Revista de Traductología :27  pp. 289 ff. DOI logo

This list is based on CrossRef data as of 23 march 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.