The amount of published information is growing rapidly. Much of this information comes in unstructured textual form that cannot easily be searched, mined, visualized and, ultimately, acted upon. The principal goal of our group are machines that can read and "understand" this textual information, converting it into interpretable structured knowledge to be leveraged by humans and other machines alike.
To achieve our goal we work in the intersection of Natural Language Processing, Machine Learning, Cognitive Science and Information Retrieval. We rely heavily on statistical methods of various flavours.
Our group is part of the UCL Computer Science department, affiliated with CSML and based in the London Media Technology Campus. We are organizing the South England Stat NLP Meetup. Get in touch if you're interested in attending.
Sebastian works in NLP and Machine Learning. He is particularly interested in helping machines to read more accurately by leveraging knowledge gathered through reading more accurately.
Ivan is interested in Graphical Models, specially in Learning Bayesian Networks and comparing them with Tensor-based Models.
Marzieh is interested in urban knowledge extraction and in particular spatial analysis of language data extracted from social media..
Matko's interest include the theory and application of machine learning (ML) methods, data, graph and text mining, ML in bioinformatics, graph theory, information retrieval and natural language processing.
Tim is interested in representation learning for NLP and automated knowledge base completion, and how such methods can take advantage of symbolic background knowledge.
George’s interests include Probabilistic Graphical Models, Natural Language Processing and Computational Linguistics, Information Retrieval and Software Development.
Andreas is working on automated fact checking in collaboration with the BBC. He is broadly interested in natural language understanding (e.g. information extraction, semantic parsing) and in machine learning approaches that would help us towards this goal.
Jason is working in collaboration with Google on leveraging knowledge bases of semantic relations and the web's vast quantity of unstructured text to guide the learning of latent variable models for NLP. He is interested in joint inference, graphical models, and natural language acquisition.