I am a research associate at the Institute of Language, Logic and Cognition (ILCC), University of Edinburgh, working with Mirella Lapata and Shay Cohen.
From July to September 2017 I will be a visiting scholar in Michael Frank's Language and Cognition Lab at Stanford University.
I use machine learning methods and computational models to gain a deeper understanding of the structure and dynamics of meaning representations both in language and in humans. I develop Bayesian models of how children learn categories and their structured featural representations in a joint and incremental process. I am also interested in the dynamics of meaning in natural language. I have developed models of word meaning change over centuries. Most recently I have used deep learning methods to model the structure and development of plots in books and films.
Informatics Forum, Room 3.38
10 Crichton Street
l.frermann [at] ed [dot] ac [dot] uk
|2017 - now||Research associate at ILCC, University of Edinburgh (collaborators Mirella Lapata and Shay Cohen)|
|2017||Visiting scolar at Language and Cognition Lab, Stanford University (host Michael Frank)|
|2013 - 2017||PhD at ILCC, University of Edinburgh (supervisors Mirella Lapata and Charles Sutton)|
|2016||Machine Learning Internship with Amazon Berlin (3 months)|
|2010 - 2013||MSc in Language Science and Technology from Saarland University (supervisors Ivan Titov and Manfred Pinkal)|
|2012||Erasmus Mundus Research exchange to NTU Singapore. Research project with Francis Bond.|
|2007 - 2010||BA in Linguistics, University of Bremen, Germany.|
- Lea Frermann and Mirella Lapata, (2016) A Bayesian Model of Diachronic Meaning Change, Transactions of the Association for Computational Linguistics (TACL).
- Lea Frermann and Mirella Lapata, (2016) Incremental Bayesian Category Learning from Natural Language, Cognitive Science.
Lea Frermann and Gyuri Szarvas, (2017) Inducing Semantic Micro-Clusters from Deep Multi-View
Representations of Novels, In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP) 2017, Copenhagen, Denmark. (to appear)
- Lea Frermann and Mirella Lapata, (2015) A Bayesian Model for Joint Learning of Categories and their Features, In Proceedings of NAACL-HLT 2015, Denver, Colorado, USA.
- Lea Frermann and Mirella Lapata, (2014) Incremental Bayesian Learning of Semantic Categories, In Proceedings of EACL 2014, Gothenburg, Sweden.
- Lea Frermann, Ivan Titov and Manfred Pinkal, (2014) A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge, In Proceedings of EACL 2014, Gothenburg, Sweden.
- Lea Frermann and Francis Bond, (2012) Cross-lingual Parse Disambiguation based on Semantic Correspondence, In Proceedings of ACL 2012, Jeju, Republic of Korea.
Non-published Workshop Contributions
- Lea Frermann, (2016) A Bayesian Model of Joint Category and Feature Learning 11th Workshop for Women in Machine Learning (WiML) in conj. with NIPS, Barcelona, Spain.
- Lea Frermann, (2015) A Bayesian Model of the Temporal Dynamics of Word Meaning 10th Workshop for Women in Machine Learning (WiML) in conj. with NIPS, Montreal, Canada.
- Lea Frermann, (2017) Bayesian Models of Category Acquisition and Meaning Development, PhD Thesis, University of Edinburgh, Scotland, UK.
- Lea Frermann, (2013) A Hierarchical Bayesian Model for Unsupervised Learning of Script Knowledge, MSc Thesis, Saarland University, Germany.
Lea Frermann, (2010) Information Extraction from Written Natural Language Input to Interactive Wayfinding Systems BA Thesis, University of Bremen, Germany.
Invited Talks and Presentations
- Keynote talk at the Drift-a-LOD workshop (co-located with EKAW), Bologna, November 2016.
Title: Modelling fine-grained Change in Word Meaning over centuries from Large Collections of Unstructured Text
Invited talk at Heriot-Watt University (Edinburgh), Machine Learning Invited Talks, (2015)
Title: Incremental Bayesian Learning of Semantic Categories and their Features
- Attended Google NLP PhD Summit, Zürich, (2015).
Invited Paper at the First Workshop on Multilingual Modeling (in conjunction with ACL 2012), Jeju, Korea, (2012).
Title: Cross-lingual Parse Disambiguation based on Semantic Correspondence
- An implementation of the Kalman filter for the static voltometer model and the canonball model (written in Go).