I am a third year PhD Student in the Computer Laboratory's Natural Language and Information Processing Group, supervised by Anna Korhonen. I have recently spent time working at the LISA lab, Montreal, with Yoshua Bengio.
I work on models and algorithms for extracting and representing semantic knowledge from text and other naturally occurring data. I like to read and take inspiration from cognitive psychology and neuroscience. I also interested in approaches to abstraction in language and algorithms for learning abstract concepts.
Because cross-disciplinary work is not as common as it should be, I am a student organizer for the Cambridge Language Sciences initiative.
When I'm not doing worky stuff I like travelling, running, football, tennis and relaxing. I was also also Captain of the Cambridge University Stymies Golf Team, who enjoy doling out punishment to my former university.
Hill, F. Cho, KH., Korhonen, A., and Bengio, Y. in press. Learning to Understand Phrases by Embedding the Dictionary. 2016. Transactions of the Association for Computational Linguistics (TACL). | Training data | | Evaluation data | | Source code | | Demo |
Hill, F. Reichart, R. Korhonen, A. 2015. SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation. Computational Linguistics Accompanying dataset.
Bentz, C., Verkerk, A., Kiela, D., Hill, F. & Buttery, P. Adaptive Languages: Modelling the Co-Evolution of Population Structure and Lexical Diversity. PLOS One.
Hill, F. Reichart, R. Korhonen, A. 2014. Multi-Modal Models for Concrete and Abstract Concept Meaning. Transactions of the Association for Computational Linguistics (TACL).
Hill, F. , Korhonen, A. & Bentz, C. 2013. A quantitative empirical analysis of the abstract/concrete distinction. Cognitive Science.
Bentz, C., Kiela, D., Hill, F. & Buttery, P. 2014. Zipf's law and the grammar of languages: A quantitative study of Old and Modern English parallel texts. Corpus Linguistics and Linguistic Theory.
Hill, F. Cho, K. & Korhonen, A. 2016. Learning Distributed Representations of Sentences from Unlabelled Data. NAACL 2016 | FastSent and DSAE Code | | FastSent web demo |
Hill, F. Bordes, A. Chopra, S. & Weston, J. 2016. The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations. ICLR 2016 (Oral) | Children's Book Data | Public Reaction |
Kiela, D., Hill, F. & Clark, S. 2015. Specializing Word Embeddings for Similarity or Relatedness. EMNLP 2015
Hill, F. Cho, KH. Jean, S. Devin, C. Bengio, Y. 2014. Embedding Word Similarity With Neural Machine Translation. Workshop Paper at ICLR 2015 Download Embeddings Here
Hill, F. & Korhonen, A. 2014. Learning Abstract Concepts from Multi-Modal Data: Since You Probably Can't See What I Mean. EMNLP 2014. VIDEO PRESENTATION
Kiela, D. & Hill, F. (joint first authors), Korhonen, A. & Clark, S. 2014. Improving multi-modal representations using image dispersion: Why less is sometimes more. ACL 2014.
Hill, F. & Korhonen, A. 2014. Concreteness and subjectivity as dimensions of lexical meaning. ACL 2014. VIDEO PRESENTATION
Hill, F., Kiela, D., & Korhonen, A. 2013. Concreteness and corpora: A theoretical and practical analysis. ACL-CMCL 2013. Cognitive Science Society Best Student Paper award (CMCL).
Hill, F., Korhonen, A., & Bentz, C. 2013. Large-scale empirical analyses of concreteness. CogSci 2012.
Hill, F. 2012. Beauty before age: Applying subjectivity to English adjective ordering. NAACL-HLT Student Research Workshop 2012.
New York University Language, the Next Big Challenge for AI. 2015
Microsoft Research, Cambridge Deep Learning and Representing Natural Language Semantics. 2015
London Machine Learning Meetup Language Understanding with Deep Neural Nets. 2015
South England NLP Meetup Deep Consequences: Why Neural Nets are Good for Science and Technology. 2014