Allott, N., and Textor, M. 2012. Lexical Pragmatic Adjustment and the Nature of Ad Hoc Concepts. International Review of Pragmatics 4(2).10.1163/18773109-00040204
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. CoRR abs/1502.03520.
Baroni, M., and Lenci, A. 2010. Distributional Memory: A General Framework for Corpus Based Semantics. Computational Linguistics 36(4):673–721.10.1162/coli_a_00016
Baroni, M.; Dinu, G.; and Kruszewski, G. 2014. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 238–247. Baltimore, Maryland: Association for Computational Linguistics.10.3115/v1/P14-1023
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., Theories of Memory. Hove: Lawrence Erlbaum Associates. 29–101.
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. Computational Linguistics 18(4):467–479.
Cimiano, P.; Staab, S.; and Tane, J. 2003. Automatic acquisition of taxonomies from text: FCA meets NLP. In In Proceedings of ECML/PKDD Workshop on Adaptive Text Extraction and Mining.
Clark, S. 2015. Vector Space Models of Lexical Meaning. In Lappin, S., and Fox, C., eds., The Handbook of Contemporary Semantic Theory. Wiley-Blackwell.10.1002/9781118882139.ch16
Collobert, R., and Weston, J. 2008. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. In Proceedings of the 25 th International Conference on Machine Learning.10.1145/1390156.1390177
Deerwester, S.; Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; and Harshman, R. 1990. Indexing by Latent Semantic Analysis. Jounal of the American Society for Information Science 41(6):391–407.10.1002/(SICI)1097-4571(199009)41:6<;391::AID-ASI1>3.0.CO;2-9
Grefenstette, E., and Sadrzadeh, M. 2011. Experimental Support for a Categorical Compositional Distributional Model of Meaning. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing.
Hassan, S., and Mihalcea, R. 2011. Semantic Relatedness Using Salient Semantic Analysis. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence.10.1609/aaai.v25i1.7971
Hill, F.; Korhonen, A.; and Bentz, C. 2014. A Quantitative Empirical Analysis of the Abstract/Concrete Distinction. Cognitive Science 38:162–177.10.1111/cogs.1207623941240
Huang, E. H.; Socher, R.; Manning, C. D.; and Ng, A. Y. 2012. Improving Word Representations via Global Context and Multiple Word Prototypes. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 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 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing, 53–60.
Lapesa, G., and Evert, S. 2013. Evaluating Neighbor Rank and Distance Measures as Predictors of Semantic Priming. In Proceedings of the ACL Workshop on Cognitive Modeling and Computational Linguistics.
Levy, O.; Goldberg, Y.; and Dagan, I. 2015. Improving Distributional Similarity with Lessons Learned from Word Embeddings. Transactions of the Association for Computational Linguistics 3.10.1162/tacl_a_00134
Lund, K., and Burgess, K. 1996. Producing High-Dimensional Semantic Spaces from Lexical Co-Occurrence. Behavior Research Methods, Instruments, and Computers 28(2):203–208.10.3758/BF03204766
Mikolov, T.; Yih, W.-T.; and Zweig, G. 2013. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 246–251.
Milajevs, D.; Kartsaklis, D.; Sadrzadeh, M.; and Purver, M. 2014. Evaluating Neural Word Representations in Tensor-Based Compositional Settings. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 708–719. Doha, Qatar: Association for Computational Linguistics.10.3115/v1/D14-1079
Moffat, D., and Kelly, M. 2006. An investigation into people’s bias against computational creativity in music composition. In Proceedings of the International Joint Workshop on Computational Creativity.
Padó, S., and Lapata, M. 2007. Dependency-Based Construction of Semantic Space Models. Computational Linguistics 33(2):161–199.10.1162/coli.2007.33.2.161
Pearce, M. T.; Müllensiefen, D.; and Wiggins, G. A. 2010. The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception 39(10):1367–1391.10.1068/p650721180358
Pennington, J.; Socher, R.; and Manning, C. D. 2014. GloVe: Global Vectors for Word Representation. In Conference on Empirical Methods in Natural Language Processing.10.3115/v1/D14-1162
Rychlý, P., and Kilgarriff, A. 2007. An efficient algorithm for building a distributional thesaurus (and other Sketch Engine developments). In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, 41–44. Prague, Czech Republic: Association for Computational Linguistics.10.3115/1557769.1557783
Snow, R.; Jurafsky, D.; and Ng, A. Y. 2006. Semantic Taxonomy Induction from Heterogenous Evidence. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 801–808. Sydney, Australia: Association for Computational Linguistics.10.3115/1220175.1220276
Socher, R.; Huval, B.; Manning, C. D.; and Ng, A. Y. 2012. Semantic Compositionality Through Recursive Matrix-vector Spaces. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 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. Journal of Artificial Intelligence Research (37):141–188.10.1613/jair.2934
Wiggins, G. A. 2006. A Preliminary Framework for Description, Analysis and Comparison of Creative Systems. Journal of Knowledge Based Systems 19(7):449–458.10.1016/j.knosys.2006.04.009
Yogatama, D.; Faruqui, M.; Dyer, C.; and Smith, N. A. 2015. Learning Word Representations with Hierarchical Sparse Coding. In Proceedings of the 32nd International Conference on Machine Learning.