Abstract
Distributional semantic models (DSMs) model semantic relations between expressions by comparing the contexts in which these expressions occur. This paper presents an extensive evaluation of distributional semantic models for Croatian language. We focus on random indexing models, an efficient and scalable approach to building DSMs. We build a number of models with different parameters (dimension, context type, and similarity measure) and compare them against human semantic similarity judgments. Our results indicate that even low-dimensional random indexing models may outperform the raw frequency models, and that the choice of the similarity measure is most important.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baroni, M., Lenci, A.: One distributional memory, many semantic spaces. In: Proceedings of the EACL Workshop on Geometrical Models of Natural Language Semantics (2009)
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: KDD 2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
Broda, B., Derwojedowa, M., Piasecki, M., Szpakowicz, S.: Corpus-based semantic relatedness for the construction of polish wordnet. In: Proceedings of the Sixth International Language Resources and Evaluation, LREC 2008 (2008)
Broda, B., Piasecki, M.: Supermatrix: a general tool for lexical semantic knowledge acquisition. In: Speech and Language Technology, vol. 11, pp. 239–254. Polish Phonetics Assocation (2008)
Burgess, C., Lund, K.: Modelling parsing constraints with high-dimensional context space. Language and Cognitive Processes 12, 1–34 (1997)
Curran, J.: From Distributional to Semantic Similarity. Ph.D. thesis, University of Edinburgh (2008)
Evert, S., Lenci, A.: Foundations of distributional semantic models, http://wordspace.collocations.de/lib/exe/fetch.php/course:acl2010:naacl2010_part1.slides.pdf (2010)
Kanerva, P.: Sparse Distributed Memory. MIT Press, Cambridge (1988)
Kilgarriff, A., Rychly, P., Smrz, P., Tugwell, D.: The sketch engine. In: Proceedings of the 11th EURALEX International Congress, pp. 105–116 (2004)
Landauer, T., Dumais, S.: A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104(2), 211–240 (1997)
Lenci, A.: Distributional semantics in linguistic and cognitive research. Italian Journal of Linguistics 20(1), 1–31 (2008)
Ljubešić, N., Boras, D., Bakarić, N., Njavro, J.: Comparing measures of semantic similarity. In: Proceedings of the ITI 2008 30th International Conference of Information Technology Interfaces (2008)
Mitrofanova, O., Mukhin, A., Panicheva, P., Savitsky, V.: Automatic word clustering in Russian texts. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 85–91. Springer, Heidelberg (2007)
Nakov, P.: Latent semantic analysis for bulgarian literature. In: Proceedings of Spring Conference of Bulgarian Mathematicians Union. Borovetz (2001)
Nakov, P.: Latent semantic analysis for russian literature investigation. In: Proceedings of the 120 years Bulgarian Naval Academy Conference, Citeseer (2001)
Pado, S., Lapata, M.: Dependency-based construction of semantic space models. Computational Linguistics 33(2), 161–199 (2007)
Piasecki, M.: Automated extraction of lexical meanings from corpus: A case study of potentialities and limitations. In: Representing Semantics in Digital Lexicography. Innovative Solutions for Lexical Entry Content in Slavic Lexicography, pp. 32–43. Institute of Slavic Studies, Polish Academy of Sciences (2009)
Sahlgren, M.: An introduction to random indexing. In: Proceedings of the Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering (2005)
Sahlgren, M.: The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces. Ph.D. thesis, Department of Linguistics, Stockholm University (2006)
Sahlgren, M.: The distributional hypothesis. Rivista di Linguistica 20(1) (2008)
Smrž, P., Rychlỳ, P.: Finding semantically related words in large corpora. In: Matoušek, V., Mautner, P., Mouček, R., Tauser, K. (eds.) TSD 2001. LNCS (LNAI), vol. 2166, pp. 108–115. Springer, Heidelberg (2001)
Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research 37, 141–188 (2010)
Šnajder, J., Dalbelo Bašić, B., Tadić, M.: Automatic acquisition of inflectional lexica for morphological normalisation. Information Processing and Management 44(5), 1720–1731 (2008)
Wilks, Y., Charniak, E.: Computational Semantics: An Introduction to Artificial Intelligence and Natural Language Understanding. North-Holland, Amsterdam (1976)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Janković, V., Šnajder, J., Dalbelo Bašić, B. (2011). Random Indexing Distributional Semantic Models for Croatian Language. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-23538-2_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23537-5
Online ISBN: 978-3-642-23538-2
eBook Packages: Computer ScienceComputer Science (R0)