The amount of published information is growing rapidly. Much of this information comes in the form of unstructured text which cannot easily be searched, mined, visualized or, ultimately, acted upon. The principal goal of our group is to build 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 and Machine Learning. 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 Natural Language Processing 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.
I am working on learning interpretable models, such as decision trees and Bayesian networks, from Matrix Factorization models. I'm interested in probabilistic graphical models. I'm funded by CONACYT.
Marzieh is interested in urban knowledge extraction and in particular spatial analysis of language data extracted from social media.
Matko interests include both natural and unnatural language processing, and their interplay. Specifically, he's enjoying differentiable abstract machines and interpreters, code induction, and trainable combinations of neural networks and code. When tired from unnatural language, he can be found enjoying a good question answering model.
Tim is interested in representation learning for natural language processing and automated knowledge base construction. His research concerns the intersection between deep learning and first-order logic, as well as natural language inference.
I am working on Multi-Instance Text Regression and learning weakly supervised word embeddings. I am interested in structured prediction, distributional semantics, neural models and optimisation. My secondary supervisor is Steffen Petersen and I am funded by the Farr Institute of Health Informatics Research.
Ingolf researches into the intersection of NLP and Information Security. His work combines topic models, sentiment analysis and statistical tests to transcripts on security topics, attempting to automatically infer conflicts between security and business processes.
Pontus works somewhere in the intersection between Natural Language Processing and Machine Learning. He is particularly interested in representation learning and is currently funded by a machine reading grant from the Allen Foundation.
I'm interested in using Machine Learning for NLP, in particular learning distributed representations of words, phrases and facts as well as their composition. Currently I'm exploring Tensor Factorization models for Knowledge Base Completion.
Isabelle works on Machine Reading of scientific articles in collaboration with Elsevier. She is interested in knowledge base population, weakly supervised learning and structured prediction.
Jeff is a researcher in the SUMMA project. He is working on the problem of knowledge base population using factorisations of the matrix.
Pasquale is interested in Machine Reading, and how to leverage background knowledge in representation learning algorithms. He is currently funded by a machine reading grant from the Allen Foundation.
Théo works on complex-valued embedding models for knowledge base completion and distributed word representations.
Tim is interested in automated knowledge base construction via weakly supervised learning and algorithms that reason about questions using raw text or knowledge base relations.
Now a lecturer @ University of Sheffield.
Now a PhD student @ MIT.
Now a post-doc @ University of Sheffield.
Now a master student @ Tohoku University.
Now a post-doc at University of Ghent
Now a post-doc at University of Cambridge