Abstract
Many connectionist language processing models have now reached a level of detail at which more realistic representations of semantics are required. In this paper we discuss the extraction of semantic representations from the word co-occurrence statistics of large text corpora and present a preliminary investigation into the validation and optimisation of such representations. We find that there is significantly more variation across the extraction procedures and evaluation criteria than is commonly assumed.
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References
Battig WF and Montague WE. Category norms for verbal items in 56 categories: A replication and extension of the Connecticut category norms. Journal of Experimental Psychology Monograph 1969; 80
Bullinaria JA. Modelling Reading, Spelling and Past Tense Learning with Artificial Neural Networks. Brain and Language 1997; in press
Bullinaria JA. Modelling Lexical Decision: Who needs a lexicon? In Keating JG. (Ed) Neural Computing Research and Applications III, 62–69. Maynooth, Ireland: St. Patrick’s College, 1995
Bullinaria JA. Connectionist Models of Reading: Incorporating Semantics. In Proceedings of the First European Workshop on Cognitive Modelling, 224–229, Berlin: Technische Universitat Berlin, 1996
Bullinaria JA and Huckle CC. Modelling Lexical Decision Using Corpus Derived Semantic Representations in a Connectionist Network. In Proceedings of the Fourth Neural Computational and Psychology Workshop 1997
Coltheart M, Curtis B, Atkins P and Haller M. Models of Reading Aloud: Dual-Route and Parallel-Distributed-Processing Approaches, Psychological Review 1993; 100: 589–608
Hinton GE and Shallice T. Lesioning an Attractor Network: Investigations of Acquired Dyslexia. Psychological Review 1991; 98: 74–95
Landauer TK and Dumais ST. A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge. Psychological Review 1997; 104: 211–240
Leech G. 100 million words of English: the British National Corpus. Language Research 1992, 28:1–13
Levy JP, Bullinaria JA and Patel M. Evaluating the Use of Word Co-Occurrence Statistics as Semantic Representations, in preparation
Lund K, Burgess C and Atchley RA. Semantic and Associative Priming in High-dimensional Semantic Space. In Moore JD and Lehman JF (Eds), Proceedings of the Seventeenth Annual Meeting of the Cognitive Science Society, 660–665. Lawrence Erlbaum Associates, Pittsburgh PA 1995
Lund K and Burgess C. Producing High-dimensional Semantic Spaces from Lexical Co-occurrence. Behaviour Research Methods, Instruments and Computers 1996; 2: 203–208
Miller GA and Fellbaume C. Semantic networks of English. Cognition 1991; 41: 197–229
Moss HE, Ostrin RK, Tyler LK and Marslen-Wilson WD. Accessing Different Types of Lexical Semantic Information: Evidence From Priming. Journal of Experimental Psychology: Learning, Memory and Cognition 1995; 21: 863–883
Patel M. Using Neural Nets to Investigate Lexical Analysis. PRICAI’96: Topics in Artificial Intelligence 1996; 241–252
Plaut DC. Semantic and Associative Priming in a Distributed Attractor Network. Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society 1995; 37–42
Plaut DC and Shallice T. Deep Dyslexia: A case study of connectionist neuropsychology. Cognitive Neuropsychology 1993; 10: 377–500
Plaut DC, McClelland JL, Seidenberg MS and Patterson KE. Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains. Psychological Review 1996; 103: 56–115
Schutze H. Word Space. In Hanson SJ, Cowan JD and Giles CL (Eds), Advances in Neural Information Processing Systems 5, 895–902. Morgan Kaufmann, San Mateo CA, 1993.
Seidenberg MS and McClelland JL. A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review 1989; 96: 523–568
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© 1998 Springer-Verlag London Limited
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Patel, M., Bullinaria, J.A., Levy, J.P. (1998). Extracting Semantic Representations from Large Text Corpora. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_16
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DOI: https://doi.org/10.1007/978-1-4471-1546-5_16
Publisher Name: Springer, London
Print ISBN: 978-3-540-76208-9
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