Gabriel Recchia

Project

The availability of large textual datasets in recent years has enabled researchers to investigate the statistical structure of language on a scale not possible in previous decades. By combining tools from cognitive science with computational and statistical techniques for analyzing large databases of text, researchers have developed computational methods for automatically finding clusters of words that are related in meaning. Using these advances as a starting point, my research at CRASSH aims to develop novel computational methods that can be used to investigate historical cultural concepts, to track how they change over time, and to characterize their properties. Specific interests include concepts of place and space, abstract concepts, and methods for combining language-based statistics with other sources of information.

About

Dr. Gabriel Recchia is a Research Associate with the Concept Lab, part of the Cambridge Centre for Digital Knowledge at CRASSH.

Formerly, Dr. Recchia was a postdoctoral fellow at the Institute for Intelligent Systems at the University of Memphis. He received his PhD in Cognitive Science from Indiana University, where he developed computational models of lexical semantics as part of the Cognitive Computing Laboratory. He has articles published or forthcoming in Behavior Research Methods, The Quarterly Journal of Experimental Psychology, and Frontiers in Human Neuroscience, among other venues.

Publications

Recchia, G., & Louwerse, M. (2014). Grounding the ungrounded: Estimating locations of unknown place names from linguistic associations and grounded representations. Proceedings of the 36thAnnual Conference of the Cognitive Science Society (pp. 1270-1275).

Recchia, G. L., & Louwerse, M. M. (2013). A comparison of string similarity measures for toponym matching. Proceedings of ACM SIGSPATIAL CoMP ’13. Orlando, FL: ACM.

Recchia, G. L., & Jones, M. N. (2012). The semantic richness of abstract concepts. Frontiers in Human Neuroscience, 6(315). doi: 10.3389/fnhum.2012.00315

Jones, M. N., Johns, B. T., Recchia, G. L. (2012). The role of semantic diversity in lexical organization.  Canadian Journal of Experimental Psychology, 66, 121-132.

Hard, B., Recchia, G., & Tversky, B. (2011). The shape of action. Journal of Experimental Psychology: General, 140(4), 586-604.

Cox, G., Kachergis, G., Recchia, G., & Jones, M. N. (2011). Towards a scalable holographic representation of word form. Behavior Research Methods, 43(3), 602-615.

Jones, M. N., Gruenenfelder, T. M., & Recchia, G. (2011). In defense of spatial models of lexical semantics. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 3444-3449).

Kachergis, G., Recchia, G., & Shiffrin, R. M. (2011). Adaptive magnitude and valence biases in a dynamic memory task. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 819-824).

Recchia, G. L., Jones, M. N., Sahlgren, M., & Kanerva, P. (2010). Encoding sequential information in vector space models of semantics: Comparing holographic reduced representation and random permutation. In S. Ohlsson and R. Catrambone (Eds.), Proceedings of the 32nd Cognitive Science Society (pp. 865-870).

Jones, M. N., & Recchia, G. L. (2010). You can’t wear a coat rack: A binding framework to avoid illusory feature migrations in perceptually grounded semantic models. In S. Ohlsson and R. Catrambone (Eds.), Proceedings of the 32nd Annual Cognitive Science Society (pp. 877-882).

Recchia, G., & Jones, M. N. (2009). More data trumps smarter algorithms: Training computational models of semantics on very large corpora. Behavior Research Methods, 41(3), 647-656.

Recchia, G., Johns, B. T., & Jones, M. N. (2008). Context repetition benefits are dependent on context redundancy. In V. Sloutsky, K. McRae, & B. Love (Eds.), Proceedings of the 30th Cognitive Science Society (pp. 267-272).

 

Position:

Concept Lab Research Associate

Period:

October 2014 - Present

Email:

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