LSAfun - An R package for computations based on Latent Semantic Analysis

Behavior Research Methods

Volumn 47, Issue 4, 2014, Pages 930-944

  Günther, Fritz ^a   Dudschig, Carolin ^a   Kaup, Barbara ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

Computer software; Distributional semantics; Latent semantic analysis; R

Indexed keywords

COMPUTER PROGRAM; HUMAN; THEORETICAL MODEL;

HUMANS; MODELS, THEORETICAL; SOFTWARE;

EID: 84947024951     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-014-0529-0     Document Type: Article

References (42)

1. Adler, D., Murdoch, D., et al. (2014). Rgl: 3d visualization device system (opengl) [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=rgl (R package version 0.93.996).
2. Bingham, E., & Mannila, H. (2001). Random projection in dimensionality reduction: Applications to image and text data. In: KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, (pp. 245–250).
3. Dennis, S (2007). How to use the LSA website. In T.K. Landauer, D.S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 57–70). Mahwah, NJ: Erlbaum.
4. Derweester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., Harshman, R. (1991). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41, 391– 407.
5. Dinu, G., Pham, N., Baroni, M. (2013). DISSECT: Distributional semantics composition toolkit. Proceedings of the system demonstrations of acl 2013 (51st annual meeting of the association for computational linguistics) (pp. 31–36). East Stroudsburg, PA: ACL.
6. Dumais, S. T. (1991). Improving the retrieval of information from external sources. Behavior Research Methods, Instrumentation, and Computers, 23, 229–236.
7. Dumais, S. T., & Nielsen, J. (1992). Automating the assignment of submitted manuscripts to reviewers. In N. Belkin, P. Ingwersen, & A.M. Pejtersen (Eds.), Proceedings of the Fifteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 233–244). New York, NY: ACM.
8. Gong, Y., & Liu, X. (2001). Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis. Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 19–25). New York, NY: ACM.
9. Günther, F. (2014). LSAfun: Applied latent semantic analysis (lsa) functions [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=LSAfun (R package version 0.3.1).
10. Harris, Z. (1954). Distributional structure. Word, 10, 146–162.
11. Hutchison, K. A., Balota, D. A., Cortese, M., Watson, J. M. (2008). Predicting semantic priming at the item level. Quarterly Journal of Experimental Psychology, 61, 1036–1066.
12. Jones, M. N., Kintsch, W., Mewhort, D. J. K., High-dimensional semantic space accounts of priming (2006). Journal of Memory and Language, 55, 534–552.
13. Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1–37.
14. Jones, M. N., Willits, J. A., Dennis, S. (in press). Models of semantic memory. In J.R. Busemeyer, & J.T. Townsend (Eds.), Oxford handbook of mathematical and computational psychology. New York, NY: Oxford University Press.
15. Jurgens, D., & Stevens, K. (2010). The S-Space package: An open source package for word space models. Proceedings of the ACL 2010 System Demonstrations (pp. 30–35). Uppsala, Sweden: ACL.
16. Kanerva, P. (1988). Sparse distributed memory. Cambridge, MA: MIT Press.
17. Kievit-Kylar, B., & Jones, M. N. (2012). Visualizing multiple word similarity measures. Behavior Research Methods, 44, 656– 674.
18. Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York, NY: Cambridge University Press.
19. Kintsch, W. (2001). Predication. Cognitive Science, 25, 173–202.
20. Laham, D. (1997). Latent semantic analysis approaches to categorization. In M.G. Shafto, & P. Langley (Eds.), Proceedings of the 19th Annual Conference of the Cognitive Science Society (p. 979). Mahwah, NJ: Erlbaum.
21. Landauer, T. K. (2007). LSA as a Theory of Meaning. In T.K. Landauer, D.S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis (pp. 3–34). Mahwah, NJ: Erlbaum.
22. Landauer, T. K., & Dumais, S. T. (1997). A solution to Platos problem: The Latent Semantic Analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240.
23. Landauer, T. K., Foltz, P. W., Laham, D. (1998). Introduction to Latent Semantic Analysis. Discourse Processes, 25, 259–284.
24. Landauer, T. K., McNamara, D. S., Dennis, S., Kintsch, W. (2007). Handbook of Latent Semantic Analysis. Mahwah, NJ: Erlbaum.
25. Lemaire, B., Denhière, G., Bellissens, C., Jhean-Larose, S. (2006). A computational model for simulating text comprehension. Behavior Research Methods, 38, 628–637.
26. Lenhard, W., Baier, H., Hoffmann, J., Schneider, W. (2007). Automatische Bewertung offener Antworten mittels Latenter Semantischer Analyse. Diagnostica, 53, 155–165.
27. Louwerse, M. M. (2011). Symbol interdependency in symbolic and embodied cognition. Topics in Cognitive Science, 3, 273–302.
28. Louwerse, M. M., & Zwaan, R. A. (2009). Language encodes geographical information. Cognitive Science, 33, 51–73.
29. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 201–208.
30. MacWhinney, B. (2000). The CHILDES Project: Tools for Analyzing Talk, 3rd ed. Mahwah, NJ: Erlbaum.
31. Mardia, K. V., Kent, J. T., Bibby, J. M. (1979). Multivariate Analysis. London, England: Academic Press.
32. Martin, D. I., & Berry, M. W. (2007). Mathematical foundations behind Latent Semantic Analysis. In T.K. Landauer, D.S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis (pp. 35–56). Mahwah, NJ: Erlbaum.
33. Mitchell, J., & Lapata, M. (2008). Vector-based Models of Semantic Composition. Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 236–244). Columbus, OH: ACL.
34. Mitchell, J., & Lapata, M. (2010). Composition in distributional models of semantics. Cognitive Science, 34, 1388–1439.
35. R Core Team (2013). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org/
36. Řehu̇řek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (pp. 45–50). Valletta, Malta: ELRA.
37. Sahlgren, M. (2008). The distributional hypothesis. Rivista di Linguista, 20, 33–53.
38. Wandmacher, T., Ovchinnikova, E., Alexandrov, T. (2008). Does Latent Semantic Analysis reflect human associations? European Summer School in Logic, Language and Information (ESSLLI08).
39. Widdows, D., & Cohen, T. (2010). The Semantic Vectors package: New algorithms and public tools for distributional semantics. Fourth International Conference on Semantic Computing (ieeeicsc) (pp. 9–15). Pittsburgh, PA: IEEE.
40. Widdows, D., & Ferraro, K. (2008). Semantic Vectors: A scalable open source package and online technology management application. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008). Morocco: Marracesh.
41. Wild, F. (2011). lsa: Latent Semantic Analysis [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=lsa (R package version 0.73).
42. Wolfe, M. B., Schreiner, M. E., Rehder, R., Laham, D., Foltz, P. W., Landauer, T. K., Kintsch, W. (1998). Learning from text: Matching reader and text by Latent Semantic Analysis. Discourse Processes, 25, 309–336.