Machine Reading

An NLP research group at the UCL Computer Science department teaching machines how to read.

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 Riedel Reader

    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.

  • V. Ivan Sanchez 4th year PhD Student

    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 Saeidi 4th year PhD Student

    Marzieh is interested in urban knowledge extraction and in particular spatial analysis of language data extracted from social media.

  • Matko Bosnjak 3rd year PhD Student

    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 Rocktäschel 3rd year PhD Student

    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.

  • George Spithourakis 3rd year PhD Student

    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 Becker 3rd year PhD Student

    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 Naradowsky Research Associate

    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 Stenetorp Research Associate

    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.

  • Johannes Welbl 1st year PhD Student

    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 Augenstein Research Associate

    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 Demeester Visiting Researcher

    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.

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