Eyal Sagi
ABSTRACT
This paper presents a new statistical method for detecting and tracking changes in word meaning, based on Latent Semantic Analysis. By comparing the density of semantic vector clusters this method allows researchers to make statistical inferences on questions such as whether the meaning of a word changed across time or if a phonetic cluster is associated with a specific meaning. Possible applications of this method are then illustrated in tracing the semantic change of 'dog', 'do', and 'deer' in early English and examining and comparing phonaesthemes.
- Bergen, B. (2004). The Psychological Reality of Phonaesthemes. Language, 80(2), 291--311.Google Scholar
- Berry, M. W. (1992) SVDPACK: A Fortran-77 software library for the sparse singular value decomposition. Tech. Rep. CS-92-159, Knoxville, TN: University of Tennessee. Google ScholarDigital Library
- Berry, M. W., Do, T., O'Brien, G. Vijay, K. Varadh an, S. (1993) SVDPACKC (Version 1.0) User's Guide, Tech. Rep. UT-CS-93-194, Knoxville, TN: University of Tennessee. Google ScholarDigital Library
- Bloomfield, L. (1933). Language. New York, NY: Holt, Rinehart and Winston.Google Scholar
- Campbell, L. (2004) Historical linguistics: An introduction 2nd ed. Cambridge, MA: The MIT Press.Google Scholar
- Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990) Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41, 391--407.Google ScholarCross Ref
- Ellegård, A. (1953) The Auxiliary Do: the Establishment and Regulation of its Use in English. Gothenburg Studies in English, 2. Stockholm: Almqvist and Wiksell.Google Scholar
- Firth, J. (1930) Speech. London: Oxford University Press.Google Scholar
- Firth, J. (1957) Papers in Linguistics, 1934-1951, Oxford University Press.Google Scholar
- Fodor, J. D. (1995) Comprehending sentence structure. In L. R. Gleitman and M. Liberman, (Eds.), Invitation to Cognitive Science, volume 1. MIT Press, Cambridge, MA. 209--246.Google Scholar
- Forston, B. W. (2003) An Approach to Semantic Change. In B. D. Joseph and R. D. Janda (Eds.), The Handbook of Historical Linguistics. Malden, MA: Blackwell Publishing. 648--666.Google Scholar
- Grice, H. P. (1989) {1975}. Logic and Conversation. In Studies in the Way of Words.. Cambridge, MA: Harvard University Press. 22--40.Google Scholar
- Halliday, M. A. K., & Hasan, R. (1976) Cohesion in English. London: Longman.Google Scholar
- Hock, H. H., and Joseph, B. D. (1996) Language History, Language Change, and Language Relationship: An Introduction to Historical and Comparative Linguistics. Berlin: Mouton de Gruyter.Google Scholar
- Hoey, M. (1991) Patterns of Lexis in Text. London: Oxford University Press.Google Scholar
- Hutchins, S. S. (1998). The psychological reality, variability, and compositionality of English phonesthemes. Dissertation Abstracts International, 59(08), 4500B. (University Microfilms No. AAT 9901857).Google Scholar
- Infomap {Computer Software}. (2007). http://infomap-nlp.sourceforge.net/ Stanford, CA.Google Scholar
- Kaufmann, S. (2000) Second-order cohesion. Computational Intelligence. 16, 511--524.Google ScholarCross Ref
- Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211--240.Google ScholarCross Ref
- Levin, E., Sharifi, M., & Ball, J. (2006) Evaluation of utility of LSA for word sense discrimination. In Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, New York City. 77--80. Google ScholarDigital Library
- Marcu, D (2003) Automatic Abstracting, Encyclopedia of Library and Information Science, Drake, M. A., ed. 245--256.Google Scholar
- Otis K., & Sagi E. (2008) Phonaesthemes: A Corpora-based Analysis. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.Google Scholar
- Pado, S. & Lapata, M. (2007) Dependency-based Construction of Semantic Space Models. Computational Linguistics, 33, 161--199. Google ScholarDigital Library
- Riedel E., Dexter S. L., Scharber C., Doering A. (2006) Experimental Evidence on the Effectiveness of Automated Essay Scoring in Teacher Education Cases. Journal of Educational Computing Research, 35, 267--287.Google ScholarCross Ref
- Rissanen, M. (1994) The Helsinki Corpus of English Texts. In Kytö, M., Rissanen, M. and Wright S. (eds), Corpora Across the Centuries: Proceedings of the First International Colloquium on English Diachronic Corpora. Amsterdam: Rodopi.Google Scholar
- Schütze, H. (1996) Ambiguity in language learning: computational and cognitive models. CA: Stanford.Google Scholar
- Schütze, H. (1998) Automatic word sense discrimination. Computational Linguistics 24(1):97--124. Google ScholarDigital Library
- Takayama, Y., Flournoy R., & Kaufmann, S. (1998) Information Mapping: Concept-Based Information Retrieval Based on Word Associations. CSLI Tech Report. CA: Stanford.Google Scholar
- Takayama, Y., Flournoy, R., Kaufmann, S. & Peters, S. (1999). Information retrieval based on domain-specific word associations. In Cercone, N. and Naruedomkul K. (eds.), Proceedings of the Pacific Association for Computational Linguistics (PACLING'99), Waterloo, Canada. 155--161.Google Scholar
- Traugott, E. C. (1999) The Role of Pragmatics in Semantic Change. In J. Verschueren (ed.), Pragmatics in 1998: Selected Papers from the 6th International Pragmatics Conference, vol. II. Antwerp: International Pragmatics Association. 93--102.Google Scholar
- Traugott, E. C. (2005) Semantic Change. In Encyclopedia of Language and Linguistics, 2nd ed., Brown K. ed. Oxford: Elsevier.Google Scholar
- Traugott, E. C., and Dasher R. B. (2002) Regularity in Semantic Change. Cambridge: Cambridge University Press.Google Scholar
- Widdows, D. (2003) Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In Proceedings of the joint Human Language Technology Conference and Annual Meeting of the North American Chapter of the Association for Computational Linguistics. Edmonton, Canada: Wiemer-Hastings. 197--204. Google ScholarDigital Library
- Widdows, D. (2004) Geometry and Meaning. CSLI Publications, CA: Stanford. Google ScholarDigital Library
Index Terms
- Semantic density analysis: comparing word meaning across time and phonetic space
Recommendations
Lexical semantic techniques for corpus analysis
Special issue on using large corpora: IIIn this paper we outline a research program for computational linguistics, making extensive use of text corpora. We demonstrate how a semantic framework for lexical knowledge can suggest richer relationships among words in text beyond that of simple co-...
A semantic concordance
HLT '93: Proceedings of the workshop on Human Language TechnologyA semantic concordance is a textual corpus and a lexicon so combined that every substantive word in the text is linked to its appropriate sense in the lexicon. Thus it can be viewed either as a corpus in which words have been tagged syntactically and ...
A Semantic Analysis of Suffocate Verbs
Chinese Lexical SemanticsAbstractThe information of semantic roles can reflect important characteristics of verbs. Among verbs related to animals actions, suffocate verbs have received limited attention in the fields of linguistics and language teaching. This study has selected ...
Comments