Incremental Construction of an Associative Network from a Corpus

Lemaire, Benoît and Denhière, Guy (2004) Incremental Construction of an Associative Network from a Corpus. [Conference Paper]

Full text available as:



This paper presents a computational model of the incremental construction of an associative network from a corpus. It is aimed at modeling the development of the human semantic memory. It is not based on a vector representation, which does not well reproduce the asymmetrical property of word similarity, but rather on a network representation. Compared to Latent Semantic Analysis, it is incremental which is cognitively more plausible. It is also an attempt to take into account higher-order co-occurrences in the construction of word similarities. This model was compared to children association norms. A good correlation as well as a similar gradient of similarity were found.

Item Type:Conference Paper
Keywords:associative network, corpus, semantic memory, LSA, Latent Semantic Analysis
Subjects:Computer Science > Statistical Models
Computer Science > Machine Learning
Psychology > Psycholinguistics
ID Code:3779
Deposited By:Lemaire, Benoit
Deposited On:25 Aug 2004
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Burgess, C. (1998). From simple associations to the building blocks of language: modeling meaning in memory with the HAL model. Behavior Research Methods, Instruments, & Computers, 30, 188-198.

Burgess, C., Livesay, K & Lund, K. (1998). Explorations in context space: words, sentences, discourse. Discourse Processes, 25, 211-257.

de la Haye, F. (2003). Normes d'associations verbales chez des enfants de 9, 10 et 11 ans et des adultes. L'Année Psychologique, 103, 109-130.

Edmonds, P. (1997). Choosing the word most typical in context using a lexical co-occurrence network. Meeting of the Association for Computational Linguistics, 507-509.

Foltz, P. W. (1996). Latent Semantic Analysis for text-based research. Behavior Research Methods, Instruments and Computers, 28-2, 197-202.

Frank, S.L., Koppen, M., Noordman, L.G.M. & Vonk, W. (2003). Modeling knowledge-based inferences in story comprehension. Cognitive Science 27(6), 875-910.

Glenberg, A. M. & Robertson, D. A., (2000). Grounding symbols and computing meaning: a supplement to Glenberg & Robertson. Journal of Memory and Language, 43, 379-401.

Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. Cambridge University Press.

Kontostathis, A. & Pottenger, W.M. (2002). Detecting patterns in the LSI term-term matrix. Workshop on the Foundation of Data Mining and Discovery, IEEE International Conference on Data Mining.

Landauer T.K. (2002). On the computational basis of learning and cognition: Arguments from LSA. In N. Ross (Ed.), The psychology of Learning and Motivation, 41, 43-84.

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, 211-240.

Lemaire, B. & Denhière, G. (submitted). Effects of higher-order co-occurrences on semantic similarity of words.

Levy, J.P., Bullinaria, J.A. & Patel, M. (1998). Explorations in the derivation of semantic representations from word co-occurrence statistics. South Pacific Journal of Psychology, 10, 99-111.

Levy, J.P., Bullinaria, J.A. (2001). Learning lexical properties from word usage patterns: which context words should be used? In R. French & J.P. Sougne (Eds)Connectionist Models of Learning, Development and Evolution: Proceedings of the Sixth Neural Computation and Psychology Workshop, 273-282. London:Springer.

Lowe, W. & McDonald, S. (2000). The direct route: mediated priming in semantic space. In Gernsbacher, M. A. and Derry, S. D., editors, Proceedings of the 22nd Annual Meeting of the Cognitive Science Society, 675-680, New Jersey. Lawrence Erlbaum Associates.

Prince, V. & Lafourcade, M. (2003). Mixing semantic networks and conceptual vectors: the case of hyperonymy. In Proc. of ICCI-2003 (2nd IEEE International Conference on Cognitive Informatics), South Bank University, London, UK, August 18 - 20, 121-128.

Sahlgren, M. (2001). Vector-based semantic analysis: representing word meaning based on random labels. Semantic Knowledge Acquisition and Categorisation Workshop at ESSLLI '01, Helsinki, Finland.

Sahlgren, M. (2002). Towards a flexible model of word meaning. AAAI Spring Symposium 2002. March 25-27, Stanford University, Palo Alto.

Spence, D.P. & Owens K.C. (1990). Lexical co-occurrence and association strength. Journal of Psycholinguistic Research 19, 317-330.

Steyvers, M., Shiffrin R.M., & Nelson, D.L. (in press). Word Association Spaces for predicting semantic similarity effects in episodic memory. In A. Healy (Ed.), Cognitive Psychology and its Applications: Festschrift in Honor of Lyle Bourne, Walter Kintsch, and Thomas Landauer. Washington DC: American Psychological Association.

Steyvers, M., & Tenenbaum, J. (submitted). Graph theoretic analyses of semantic networks: small worlds in semantic networks.

Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327-352.

Wolfe, M. B. W., Schreiner, M. E., Rehder, B., Laham, D., Foltz, P. W., Kintsch & W., Landauer, T. K. (1998).Learning from text: Matching readers and texts by Latent Semantic Analysis. Discourse Processes, 25, 309-336.


Repository Staff Only: item control page