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.
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.
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 Machine Learning and NLP; my research focusses on Reading Comprehension and Knowledge Base Inference. Currently I work on multi-step Reading Comprehension, a scenario in which a model combines multiple facts to arrive at an answer. Previously I've worked on Tensor Factorization models for Knowledge Base Inference.
I'm interested in using machine reading technology to extract and verify facts from raw text.
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.
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.
Galaxies, dude. Cool!
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
Now a PhD student at Xerox Research Centre Europe
Now @ Gluru
Now an assistant professor at University of Copenhagen.
Now a post-doc at University of Oxford.
Now an assistant professor @ Tohoku University.