This page is used to maintain information about our regular meetings, with links to relevant papers and other resources.
For the Spring Term 2016, The reading seminar sessions will be on deep learning and are convened by Deirdre Lungley. The research group meetings will be on recent work we have presented and are convened by Jon Chamberlain.
Venue: Colloquium room (next to Udo's office)
Spring Term 2016
12-1pm Mon 1 Feb - Cancelled
12-1pm Mon 8 Feb - Reading Seminar - Deep Learning
We will start by reviewing this blog: The Unreasonable Effectiveness of Recurrent Neural Networks
12-1pm Mon 15 Feb - Research Group Meeting - Cancelled
12-1pm Mon 22 Feb - Reading Seminar - Deep Learning
We'll be reviewing the slides on back-propagation available here: https://www.dropbox.com/sh/9xs0r0ld4lfxifa/AAATyfKN9Gbpr_af4UmalQL7a?dl=0
4-6pm Wed 24 Feb - Departmental Seminar - Sien Moens, Argumentation Mining (abstract below)
12-1pm Mon 29 Feb - Research Group Meeting directly followed by:
1-2pm Mon 29 Feb - Reading Seminar - Deep Learning
Continuing to review the above slides on back-propagation.
12-1pm Mon 7 March - Reading Seminar - Deep Learning
12-1pm Mon 14 March - Research Group Meeting (Mijail Kabadjov, CSEE)
4-6pm Wed 16 March - Departmental Seminar - Josef Steinberger - Media Gist (abstract below)
12-1pm Mon 21 March - Reading Seminar - Deep Learning
Upcoming LAC Departmental Seminars
Professor Sien Moens (University of Leuven, Belgium)
16:00 Weds 24 Feb 2016
Argumentation mining is currently in the center of attention of the text mining research community. In human discourse - whether written or spoken - argumentation always plays an important role. Arguing means that you claim that something is true and you try to persuade your audience that your claim is true by providing evidence to support your claim. Argumentation mining can be defined as the detection of the argumentative discourse structure in text or speech and the recognition or functional classification of the components of the argumentation. Argumentation mining is part of the broader field that recognises rhetorical discourse structures in text, where rhetoric is the art of discourse that aims to improve the capabilities of writers and speakers to inform, persuade or motivate particular audiences in specific situations.
The lecture will focus on the text mining methods to accomplish the structuring of the discourse and classification of argumentation components and their relations. It will discuss machine learning methods that recognize structures in discourse and methods of distributional semantics that find argumentative relations between the text segments. We illustrate the talk with our own work on argumentation recognition in court decisions.
Argumentation mining refines search and information retrieval tasks or provides the end user with instructive visualizations and summaries of an argumentative structure. The idea is to build tools that help users to quickly find arguments that sustain a certain claim or conclusion without having to read tons of information.
Marie-Francine Moens is a full professor at the Department of Computer Science at KU Leuven, Belgium. She holds a M.Sc. and a Ph.D. degree in Computer Science from this university. She is head of the Language Intelligence and Information retrieval (LIIR) research group and is a member of the Human Computer Interaction unit. She is currently also head of the Informatics section of the Department of Computer Science at KU Leuven. Her main interests are in the domain of automated content recognition in text and multimedia data and its application in information extraction and retrieval using statistical machine learning, and exploiting insights from linguistic and cognitive theories. She is currently a member of the Council of the Industrial Research Fund of KU Leuven and is the scientific manager of the EU COST action iV&L Net (The European Network on Integrating Vision and Language). She is a member of the editorial board of the journal Foundations and Trends® in Information Retrieval. In 2011 and 2012 she was appointed as chair of the European Chapter of the Association for Computational Linguistics (EACL) and was a member of the executive board of the Association for Computational Linguistics (ACL). From 2010 until 2014 she was a member of the Research Council of KU Leuven.
MediaGist: A cross-lingual analyser of aggregated news and commentaries
Dr Josef Steinberger (University of West Bohemia)
16:00 Weds 16 March 2016
MediaGist is an online system for crosslingual analysis of aggregated news and commentaries based on summarisation and sentiment analysis technologies.
It is designed to assist journalists to detect and explore news topics, which are controversially reported or discussed in different countries.
News articles from current week are clustered separately in currently 5 languages and the clusters are then linked across languages.
Sentiment analysis provides a basis to compute controversy scores and summaries help to explore the differences.
Recognized entities play an important role in most of the system’s modules and provide another way to explore the data.
I will describe the key modelues of the system and demonstrate capabilities of MediaGist by highlights from the last weeks.
Josef Steinberger is an associate professor at the Department of computer science and engineering at the University of West Bohemia, Czech Republic. He holds a M.Sc. and a Ph.D. degree in Computer science from this university. His main interests are in the domain of summarisation and sentiment analysis. From 2009 until 2012 he joined the team at the Joint Research Centre of the European Commission, Italy, to work on Europe Media Monitor. Building media monitoring solutions brought other topics of interest, like news clustering, categorisation or named entity recognition. To achieve high multilinguality his aim is to limit dependency on a particular language or building multilingual resources. He has been actively involved in the Multiling community, which organizes summarisation evaluation campaigns. In 2013, he received Marie Curie funding with the MediaGist project.
Dr Annie Louis (CSEE)
16:00 Weds 23 March 2016
London Text Analytics Meetups (usually meeting in London every couple of months)
Setup and co-organised by Professor Udo Kruschwitz, this group is for people interested in learning about and discussing topics related to text analytics (aka natural language processing). The group began life as the London GATE users group, but has since expanded to embrace other NLP platforms / toolkits and a growing interest in the use of text analytics for applications in areas such as search, social media, intelligence, life sciences, customer experience and more. They welcome both researcher and practitioner viewpoints alike.
Text Analytics · Natural Language Processing · Text Mining · Search, Information Retrieval · Speech Recognition
Next meeting: 18:00 Mon 14 March 2016 (see website for more details)