Article published In:
International Journal of Corpus Linguistics
Vol. 20:1 (2015) ► pp.5480
References (69)
Ammon, U., Bickel, H., Ebner, J., Esterhammer, R., Gasser, M., Hofer, L.,Kellermeier-Rehbein, B., Löffler, H., Mangott, D., Moser, H., Schläpfer, R., Schloßmacher, M., Schmidlin, R., & Vallaster, G. (Eds.) (2004). Variantenwörterbuch des Deutschen. Die Standardsprache in Österreich, der Schweiz und Deutschland sowie in Liechtenstein, Luxemburg, Ostbelgien und Südtirol. Berlin, Germany: Walter de Gruyter. DOI logoGoogle Scholar
Arppe, A. (2008). Univariate, bivariate and multivariate methods in corpus-based lexicography: A study of synonymy. (Unpublished doctoral dissertation). University of Helsinki, Helsinki, Finland.
Arppe, A., & Järvikivi, J. (2007). Every method counts: Combining corpus-based and experimental evidence in the study of synonymy. Corpus Linguistics and Linguistic Theory, 3(2), 131–159. DOI logoGoogle Scholar
Atkins, B., & Levin, B. (1995). Building on a corpus: A linguistic and lexicographical look at some near-synonyms. International Journal of Lexicography, 8(2), 85–114. DOI logoGoogle Scholar
Bai, J., Song, D., Bruza, P., Nie, J.-Y., & Cao, G. (2005). Query expansion using term relationships in language models for information retrieval. In O. Herzog, H. Schek, N. Fuhr, A. Chowdhury & W. Teiken (Eds.), Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM 2005) (pp. 688–695). New York, NY, ACM.Google Scholar
Baker, C.F., Fillmore, C.J., & Lowe, J.B. (1998). The Berkeley FrameNet project. In C. Boitet & P. Whitelock (Eds.), 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, COLING-ACL ‘98, Proceedings of the Conference (pp. 86–90). Stroudsburg, PA: Morgan Kaufmann Publishers/ACL.Google Scholar
Baroni, M., Lenci, A., & Onnis, L. (2007). ISA meets Lara: An incremental word space model for cognitively plausible simulations of semantic learning. In A. Lenci, M. Padró, T. Poibeau & A. Villavicencio (Eds.), Proceedings of the ACL Workshop on Cognitive Aspects of Computational Language Acquisition (pp. 49–56). Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Bertels, A., Speelman, D., & Geeraerts, D. (2006). Analyse quantitative et statistique de la sémantique dans un corpus technique. In P. Mertens, C. Fairon, A. Dister & P. Watrin (Eds.), Actes de la 13e Conférence sur le Traitement Automatique des Langues Naturelles (TALN 2006) (pp. 73–82). Louvain-la-Neuve, Belgium: Presses universitaires de Louvain.Google Scholar
Biber, D., Conrad, S., & Reppen, R. (1998). Corpus Linguistics: Investigating Language Structure and Use. Cambridge, UK: Cambridge University Press. DOI logoGoogle Scholar
Boussidan, A., Sagi, E., & Ploux, S. (2009). Phonaesthemic and etymological effects on the distribution of senses in statistical models of semantics. In Proceedings of the CogSci Workshop on Distributional Semantics beyond Concrete Concepts (DiSCo 2009) , 35–40.
Buchanan, L., Burgess, C., & Lund, K. (1996). Overcrowding in semantic neighborhoods: Modeling deep dyslexia. Brain and Cognition, 32(2), 111–114.Google Scholar
Burgess, C., Livesay, K., & Lund, K. (1998). Explorations in context space: Words, sentences, discourse. Discourse Processes, 25(2–3), 211–257. DOI logoGoogle Scholar
Chiao, Y.-C., & Zweigenbaum, P. (2002). Looking for candidate translational equivalents in specialized, comparable corpora. In S. Tseng, T. Chen & Y. Liu (Eds.), Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002), (pp. 1208–1212).Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Church, K.W., & Hanks, P. (1989). Word association norms, mutual information and lexicography. In J. Hirschberg (Ed.), Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics (ACL 1989) (pp. 76–83). Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Church, K.W., Gale, W., Hanks, P., & Hindle, D. (1991). Using statistics in lexical analysis. In U. Zernik (Ed.), Lexical Acquisition: Exploiting On-line Resources to Build a Lexicon (pp. 115–164). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Clark, S., & Weir, D. (2002). Class-based probability estimation using a semantic hierarchy. Computational Linguistics, 28(2), 187–206. DOI logoGoogle Scholar
Curran, J.R. (2004). From distributional to semantic similarity. (Unpublished doctoral dissertation). University of Edinburgh, Edinburgh, UK.
Dagan, I., Lee, L., & Pereira, F.C.N. (1999). Similarity-based models of word cooccurrence probabilities. Machine Learning, 34(1–3), 43–69. DOI logoGoogle Scholar
Den Boon, T. & Geeraerts, D. (Eds.) (2005). Van Dale Groot Woordenboek van de Nederlandse taal (14th ed.). Utrecht/Antwerp: Van Dale Lexicografie.Google Scholar
Deygers, K., & Van Den Heede, V. (2000). Belgisch Nederlandse ‘klassiekers’ als variabelen voor lexicaal variatie-onderzoek: Een evaluatie. Taal en Tongval, 52(2), 308–328.Google Scholar
Diab, M., & Finch, S. (2000). A statistical word-level translation model for comparable corpora. In J. Mariani & D. Harman (Eds.), Proceedings of the 6th Conference on Content-Based Multimedia Information Access (RIAO 2000) (pp. 1500–1508). Paris, France: Collège de France.Google Scholar
Divjak, D., & Gries, S. Th. (2006). Ways of trying in Russian: Clustering behavioral profiles. Journal of Corpus Linguistics and Linguistic Theory, 2(1), 23–60.Google Scholar
Firth, J.R. (1957). A synopsis of linguistic theory 1930–1955. In Philological Society (Eds.), Studies in Linguistic Analysis (pp. 1–32). Oxford, UK: Blackwell.Google Scholar
Foltz, P.W. (1996). Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, and Computers, 28(2), 197–202. DOI logoGoogle Scholar
Fung, P., & McKeown, K. (1997). Finding terminology translations from nonparallel corpora. In J. Zhou & K. Church (Eds.), Proceedings of the Fifth Workshop on Very Large Corpora (pp. 192–202). Hong Kong/Beijing, China: The Hong Kong University of Science and Technology & Tsinghua University.Google Scholar
Geeraerts, D. (2010a). Lexical variation in space. In P. Auer & J.E. Schmidt (Eds.), Language and Space. An International Handbook of Linguistic Variation (pp. 820–836). Berlin, Germany: De Gruyter Mouton.Google Scholar
. (2010b). Theories of Lexical Semantics. Oxford, UK: Oxford University Press. DOI logoGoogle Scholar
Geeraerts, D., Grondelaers, S., & Speelman, D. (1999). Convergentie en Divergentie in de Nederlandse Woordenschat. Amsterdam, Netherlands: Meertens Instituut.Google Scholar
Gilquin, G. (2003). Causative ‘get’ and ‘have’: So close, so different. Journal of English Linguistics, 31(2), 125–148. DOI logoGoogle Scholar
Gries, S. Th. (2001). A corpus-linguistic analysis of -ic and -ical adjectives. ICAME Journal, 251, 65–108.Google Scholar
Gries, S. Th., & Stefanowitsch, A. (2004). Extending collostructional analysis: A corpus-based perspectives on ‘alternations’. International Journal of Corpus Linguistics, 9(1), 97–129. DOI logoGoogle Scholar
Gries, S. Th., & Otani, N. (2010). Behavioral profiles: A corpus-based perspective on synonymy and antonymy. ICAME Journal, 341, 121–150.Google Scholar
Glynn, D. (2007). Mapping meaning. Towards a usage-based methodology in Cognitive Semantics. (Unpublished doctoral dissertation). University of Leuven, Leuven, Belgium.
Glynn, D., & Fischer, K. (Eds.) (2010). Quantitative Methods in Cognitive Semantics: Corpus-driven Approaches. Berlin/New York: De Gruyter Mouton. DOI logoGoogle Scholar
Hanks, P. (1996). Contextual dependency and lexical sets. International Journal of Corpus Linguistics, 1(1), 75–98. DOI logoGoogle Scholar
Harris, Z. 1954. Distributional structure. Word, 10(2–3), 146–162. DOI logoGoogle Scholar
Janda, L., & Solovyev, V. (2009). What constructional profiles reveal about synonymy: A case study of Russian words for SADNESS and HAPPINESS. Cognitive Linguistics, 20(2), 367–393. DOI logoGoogle Scholar
Jijkoun, V., & De Rijke, M. (2005). Recognizing textual entailment: Is word similarity enough? In J. Quinonero Candela, I. Dagan, B. Magnini & F. d’Alche Buc (Eds.), Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop (MLCW 2005) (pp. 449–460). New York, NY: Springer.Google Scholar
Kilgarriff, A., & Yallop, C. (2000). What’s in a thesaurus? In M. Gavrilidou, G. Carayannis, S. Markantonatou, S. Piperidis & G. Stainhauer (Eds.), Proceedings of the 2nd Language Resources and Evaluation Conference (LREC 2000) (pp. 1371–1379). Athens, Greece: European Language Resources Association.Google Scholar
Kintsch, W. (2000). Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review, 71, 257–266. DOI logoGoogle Scholar
Labov, W. (1972). Sociolinguistic Patterns. Philadelphia, PA: University of Pennsylvania Press.Google Scholar
Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato’s problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211–240. DOI logoGoogle Scholar
Lee, L. (1999). Measures of distributional similarity. In R. Dale & K. Church (Eds.), Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL 1999) (pp. 25–32). Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Levshina, N. (2011). Doe wat je niet laten kan: A usage-based analysis of Dutch causative constructions. (Unpublished doctoral dissertation). University of Leuven, Leuven, Belgium.
Lin, D. (1998). Automatic retrieval and clustering of similar words. In C. Boitet & P. Whitelock (Eds.), 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, COLING-ACL ‘98, Proceedings of the Conference (pp. 768–774). Stroudsburg, PA: Morgan Kaufmann Publishers/ACL.Google Scholar
Lowe, W. (2001). Towards a theory of semantic space. In J.D. Moore & K. Stenning (Eds.), Proceedings of the 23rd Annual Conference of the Cognitive Science Society (CogSci 2001) (pp. 576–581). London, UK: Lawrence Erlbaum Associates.Google Scholar
Lowe, W., & McDonald, S. (2000). The direct route: Mediated priming in semantic space. In L.R. Gleitman & A.K. Joshi (Eds.), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (CogSci 2000) (pp. 675–680). London, UK: Lawrence Erlbaum Associates.Google Scholar
Martin, W. (2005). Het Belgisch-Nederlands anders bekeken: het Referentiebestand Belgisch-Nederlands (RBBN) (Technical report). Amsterdam, Netherlands: Vrije Universiteit Amsterdam.Google Scholar
McCarthy, D., Koeling, R., Weeds, J., & Carroll, J. (2004). Finding predominant word senses in untagged text. In D. Scott, W. Daelemans & M.A. Walker (Eds.), Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004) (pp. 279–286). Stroudsburg, PA: ACL.Google Scholar
Michelbacher, L., Evert, S., & Schütze, H. (2007). Asymmetric association measures. In G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov & N. Nikolov (Eds.), Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2007) (pp. 1–6). Sofia, Bulgaria: Institute for Parallel Processing, Bulgarian Academy of Sciences.Google Scholar
Mitchell, T.M., Shinkareva, S.V., Carlson, A., Chang, K.-M., Malave, V.L., Mason, R.A., & Just, M.A. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320(5880), 1191–1195. DOI logoGoogle Scholar
Padó, S., & Lapata, M. (2007). Dependency-based construction of semantic space models. Computational Linguistics, 33(2), 161–199. DOI logoGoogle Scholar
Partington, A. (1998). Patterns and Meanings: Using Corpora for English Language Research and Teaching. Amsterdam, Netherlands: John Benjamins. DOI logoGoogle Scholar
Peirsman, Y. (2008). Word space models of semantic similarity and relatedness. In K. Balogh (Ed.), Proceedings of the 13th ESSLLI Student Session (pp. 143–152). Hamburg, Germany: FoLLI.Google Scholar
Peirsman, Y., & Geeraerts, D. (2009). Predicting strong associations on the basis of corpus data. In A. Lascarides, C. Gardent & J. Nivre (Eds.), Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009) (pp. 648–656). Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Peirsman, Y., Geeraerts, D. & Speelman, D. (2010). The Automatic Identification of Lexical Variation between Language Varieties. Journal of Natural Language Engineering, 16(4), 469–491. DOI logoGoogle Scholar
Rapp, R. (1999). Automatic identification of word translations from unrelated English and German corpora. In R. Dale & K. Church (Eds.), Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL 1999) (pp. 519–526). Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Reisinger, J., & Mooney, R. (2010). Multi-prototype vector-space models of word meaning. In R.M. Kaplan, J. Burstein, M. Harper & G. Penn (Eds.), Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL 2010) (pp. 109–117). Stroudsburg, PA: ACL.Google Scholar
Ruette, T., Geeraerts, D., Peirsman, Y., & Speelman, D. (2014). Semantic weighting mechanisms in scalable lexical sociolectometry. In B. Wälchli & B. Szmrecsanyi (Eds.), Aggregating Dialectology, Typology, and Register Analysis. Linguistic Variation in Text and Speech (pp. 205–230). Berlin, Germany: De Gruyter. DOI logoGoogle Scholar
Sagi, E., Kaufmann, S., & Clark, B. (2009). Semantic density analysis: Comparing word meaning across time and phonetic space. In R. Basili & M. Pennacchiotti (Eds.), Proceedings of the EACL 2009 Workshop on GEMS: Geometrical Models of Natural Language Semantics (pp. 104–111). Stroudsburg, PA: ACL. DOI logoGoogle Scholar
Sahlgren, M. (2006). The Word-Space model. Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces. (Unpublished doctoral dissertation). Stockholm University, Stockholm, Sweden.
Soares da Silva, A. (2010). Measuring and parameterizing lexical convergence and divergence between European and Brazilian Portuguese. In D. Geeraerts, G. Kristiansen & Y. Peirsman (Eds.), Advances in Cognitive Sociolinguistics (pp. 41–83). Berlin/New York: Mouton de Gruyter. DOI logoGoogle Scholar
Speelman, D., Grondelaers, S., & Geeraerts, D. (2003). Profile-based linguistic uniformity as a generic method for comparing language varieties. Computers and the Humanities, 37(3), 317–337. DOI logoGoogle Scholar
Turney, P., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37(1), 141–188. DOI logoGoogle Scholar
Van der Plas, L. (2008). Automatic lexico-semantic acquisition for question answering. (Unpublished doctoral dissertation). University of Groningen, Groningen, Netherlands.
Wettler, M., Rapp, R., & Sedlmeier, P. (2005). Free word associations correspond to contiguities between words in texts. Journal of Quantitative Linguistics, 12(2–3), 111–122. DOI logoGoogle Scholar
Wittgenstein, L. (1953). Philosophical Investigations. Oxford, UK: Blackwell.Google Scholar
Zhitomirsky-Geffet, M., & Dagan, I. (2009). Bootstrapping distributional feature vector quality. Computational Linguistics, 35(3), 435–461. DOI logoGoogle Scholar
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