From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation
References
- Allott, N., and Textor, M. 2012. Lexical Pragmatic Adjustment and the Nature of Ad Hoc Concepts.4(2).
- Arora, S.; Li, Y.; Liang, Y.; Ma, T.; and Risteski, A. 2015. Random Walks on Context Spaces: Towards an Explanation of the Mysteries of Semantic Word Embeddings.abs/1502.03520.
- Baroni, M., and Lenci, A. 2010. Distributional Memory: A General Framework for Corpus Based Semantics.36(4):673–721.
- Baroni, M.; Dinu, G.; and Kruszewski, G. 2014. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In, 238–247. Baltimore, Maryland: Association for Computational Linguistics.
- Barsalou, L. W. 1993. Flexibility, Structure, and Linguistic Vagary in Concepts: Manifestations of Compositional System of Perceptual Symbols. In Collins, A. F.; Gathercole, S. E.; Conway, M. A.; and Morris, P. E., eds.,. Hove: Lawrence Erlbaum Associates. 29–101.
- Bengio, Y.; Ducharme, R.; Vincent, P.; and Jauvin, C. 2003. A Neural Probabilistic Language Model.3:1137–1155.
- Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2003. Latent Dirichlet Allocation.3:993–1022.
- Boden, M. A. 1990.. London: Weidenfeld and Nicolson.
- Brown, P. F.; deSouza, P. V.; Mercer, R. L.; Della Pietra, V. J.; and Lai, J. C. 1992. Class-Based n-gram Models of Natural Language.18(4):467–479.
- Carston, R. 2010. Lexical Pragmatics, Ad Hoc Concepts and Metaphor.22(1):153–180.
- Cimiano, P.; Staab, S.; and Tane, J. 2003. Automatic acquisition of taxonomies from text: FCA meets NLP. In.
- Clark, A. 2006. Language, Embodiment, and the Cognitive Niche.10(8).
- Clark, S. 2015. Vector Space Models of Lexical Meaning. In Lappin, S., and Fox, C., eds.,. Wiley-Blackwell.
- Collobert, R., and Weston, J. 2008. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. In.
- Davidson, D. 1974. On the Very Idea of a Conceptual Scheme. In, volume 47, 5–20.
- Deerwester, S.; Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; and Harshman, R. 1990. Indexing by Latent Semantic Analysis.41(6):391–407.
- Fauconnier, G., and Turner, M. 1998. Conceptual Integration Networks.22(4):133–187.
- Fellbaum, C., ed. 1998.. MIT Press.
- Gärdenfors, P. 2000.. Cambridge, MA: The MIT Press.
- Gibson, J. J. 1979.. Boston: Houghton Miffline.
- Grefenstette, E., and Sadrzadeh, M. 2011. Experimental Support for a Categorical Compositional Distributional Model of Meaning..
- Harris, Z. 1957. Co-Occurrence and Transformation in Linguistic Structure.33(3):283–340.
- Hassan, S., and Mihalcea, R. 2011. Semantic Relatedness Using Salient Semantic Analysis. In.
- Hill, F.; Korhonen, A.; and Bentz, C. 2014. A Quantitative Empirical Analysis of the Abstract/Concrete Distinction.38:162–177.
- Huang, E. H.; Socher, R.; Manning, C. D.; and Ng, A. Y. 2012. Improving Word Representations via Global Context and Multiple Word Prototypes. In, volume Long Papers: Volume 1, 873–882.
- Kanejiya, D.; Kumar, A.; and Prasad, S. 2003. Automatic Evaluation of Students Answers using Syntactically Enhanced LSA. In, 53–60.
- Koestler, A. 1964.. London, UK: Hutchinson.
- Lapesa, G., and Evert, S. 2013. Evaluating Neighbor Rank and Distance Measures as Predictors of Semantic Priming. In.
- Levy, O.; Goldberg, Y.; and Dagan, I. 2015. Improving Distributional Similarity with Lessons Learned from Word Embeddings.3
- Lund, K., and Burgess, K. 1996. Producing High-Dimensional Semantic Spaces from Lexical Co-Occurrence.28(2):203–208.
- Manning, C., and Schütze, H. 1999.. MIT Press.
- Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013. Efficient Estimation of Word Representations in Vector Space. In.
- Mikolov, T.; Yih, W.-T.; and Zweig, G. 2013. Linguistic Regularities in Continuous Space Word Representations. In, 246–251.
- Milajevs, D.; Kartsaklis, D.; Sadrzadeh, M.; and Purver, M. 2014. Evaluating Neural Word Representations in Tensor-Based Compositional Settings. In, 708–719. Doha, Qatar: Association for Computational Linguistics.
- Moffat, D., and Kelly, M. 2006. An investigation into people’s bias against computational creativity in music composition. In.
- Padó, S., and Lapata, M. 2007. Dependency-Based Construction of Semantic Space Models.33(2):161–199.
- Pearce, M. T.; Müllensiefen, D.; and Wiggins, G. A. 2010. The role of expectation and probabilistic learning in auditory boundary perception: A model comparison.39(10):1367–1391.
- Pennington, J.; Socher, R.; and Manning, C. D. 2014. GloVe: Global Vectors for Word Representation. In.
- Ritchie, G. 2007. Some empirical criteria for attributing creativity to a computer program.17(1):67–99.
- Rychlý, P., and Kilgarriff, A. 2007. An efficient algorithm for building a distributional thesaurus (and other Sketch Engine developments). In, 41–44. Prague, Czech Republic: Association for Computational Linguistics.
- Schütze, H. 1992. Dimensions of Meaning. In, 787–796.
- Snow, R.; Jurafsky, D.; and Ng, A. Y. 2006. Semantic Taxonomy Induction from Heterogenous Evidence. In, 801–808. Sydney, Australia: Association for Computational Linguistics.
- Socher, R.; Huval, B.; Manning, C. D.; and Ng, A. Y. 2012. Semantic Compositionality Through Recursive Matrix-vector Spaces. In, EMNLP-CoNLL ’12, 1201–1211. Stroudsburg, PA, USA: Association for Computational Linguistics.
- Turney, P. D., and Pantel, P. 2010. From Frequency to Meaning: Vector Space Models of Semantics.(37):141–188.
- Widdows, D. 2004.. Stanford, CA: CSLI Publications.
- Wiggins, G. A. 2006. A Preliminary Framework for Description, Analysis and Comparison of Creative Systems.19(7):449–458.
- Yogatama, D.; Faruqui, M.; Dyer, C.; and Smith, N. A. 2015. Learning Word Representations with Hierarchical Sparse Coding. In.
DOI: https://doi.org/10.1515/jagi-2015-0004 | Journal eISSN: 1946-0163
Language: English
Page range: 55 - 86
Submitted on: May 24, 2015
Accepted on: Nov 19, 2015
Published on: Dec 30, 2015
Published by: Artificial General Intelligence Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
Related subjects:
© 2015 Stephen McGregor, Kat Agres, Matthew Purver, Geraint A. Wiggins, published by Artificial General Intelligence Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.