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.


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

Paul's research focuses on applications of natural language processing to questions in digital humanities and social science. Specific interests include computational models of semantic relations between entities, and methods for extracting political concepts and ideological positions from text.
In 2013 he gained his PhD from the School of Computer Science in University College Dublin, on the topic of understanding and predicting lexical expressions of semantic relations between nouns, particularly in non-lexicalised English noun compounds.
From 2012-2015 he was a Research Officer in the Department of Methodology at the London School of Economics and Political Science, where he helped to develop (with Prof. Kenneth Benoit and others) R software for quantitative text analysis for social science. He also worked on general applications of computational text analysis to political science, such as content analysis of twitter data and ideological scaling of speeches and manifestos.


Nulty, P., Theocharis, Y., Popa, S. A., Parnet, O., & Benoit, K. (2016). Social media and political communication in the 2014 elections to the European Parliament. Electoral Studies.

Nulty, P., & Costello, F. (2013). General and specific paraphrases of semantic relations between nouns. Natural Language Engineering, 19(03), 357-384. |code|

Nulty, P., & Costello, F. (2010). UCD-PN: Selecting general paraphrases using conditional probability. In Proceedings of the 5th International Workshop on Semantic Evaluation (pp. 234-237). Association for Computational Linguistics.

Nulty, P., & Costello, F. (2009). A comparison of word similarity measures for noun compound disambiguation. In Irish Conference on Artificial Intelligence and Cognitive Science (pp. 231-240). Springer Berlin Heidelberg

Nulty, P., & Costello, F. (2009,). Using lexical patterns in the Google Web 1T corpus to deduce semantic relations between nouns. In Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (pp. 58-63). Association for Computational Linguistics.

Nulty, P. (2007). Semantic classification of noun phrases using web counts and learning algorithms. In Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop(pp. 79-84). Association for Computational Linguistics.

Nulty, P. (2007). UCD-PN: Classification of semantic relations between nominals using wordnet and web counts. In Proceedings of the 4th International Workshop on Semantic Evaluations(pp. 374-377). Association for Computational Linguistics.

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

Recchia, G. & Nulty, P. (2017). 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.


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