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, 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 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'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 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.
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, reinforcement learning, and natural language acquisition.
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.
After a PhD in Machine Learning and ten years as a researcher at Xerox Research Centre Europe, Guillaume recently joined the Machine Reading Group to pursue his long term research direction: teaching machine to understand language rather waiting that the machine learns it by itself.
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 work on Machine Reading of scientific articles in collaboration with Elsevier. She is interested in knowledge base population, weakly supervised learning and structured prediction.
Thomas joined the Machine Reading group as a visiting researcher from Ghent University. He is interested in machine learning for information retrieval and extraction, and currently works on combining logic with representation learning for relation extraction.