Nulty, Paul. (2017). “Network Visualisations for Exploring Political Concepts”. Proceedings of the 12th International Conference on Computational Semantics (IWCS).

Recchia, G. & Nulty, P. Improving a fundamental measure of lexical association. To appear in Proceedings of the 39th Annual Conference of the Cognitive Science Society.

Recchia, G., Jones, E., Nulty, P., Regan, J., & de Bolla, P. (2016). Tracing shifting conceptual vocabularies through time. In Ciancarini, P. et al. (Eds.): Knowledge Engineering and Knowledge Management: EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19–23, 2016, Revised Selected Papers (pp. 19-28). Springer International AG: Cham, Switzerland. doi: 10.1007/978-3-319-58694-6

Recchia, G. (2016). The utility of count-based models for the digital humanities. Abstract in proceedings of the Digital Humanities Congress 2016. Sheffield: HRI Online Publications, 2016.

Recchia, G. (2017). Fall and rise of AI: Computational methods for investigating cultural narratives. Invited presentation to AI Narratives: Workshop 1, Leverhulme Centre for the Future of Intelligence and the Royal Society, 16 May 2017, Hughes Hall, Cambridge.

de Bolla, P., Jones, E., Nulty, P., Recchia, G., & Regan, J. (2016). The Concept Lab. Invited presentation to The Stanford Literary Lab and Alan Liu’s research group at UC Santa Barbara.

Recchia, G. (2016). Tracing concepts through time. Invited talk at Natural Language and Information Processing Seminar Series, University of Cambridge, UK.

Recchia, G. (2016). Big data in the social sciences and the humanities. Invited talk/workshop at Cambridge AHRC Doctoral Training Partnership: Workshop on Big and Small Data, University of Cambridge, UK.

Recchia, G. (2015). Making sense of language: It’s okay to count. Invited talk at Microsoft Research Cambridge, UK.

Recchia, G. (2015). The unreasonable effectiveness of co-occurrence based models. Big Data Methods for Social Sciences and Policy, University of Cambridge, UK.