This is the wiki for the Language and Computation Group at the University of Essex. This page is used to maintain information about our regular meetings, with links to relevant papers and other resources.
If you have any questions about the Language and Computation Group; its meetings; joining our mailing list; editing pages on this wiki; or undertaking a PhD or similar in the area of language and computation — including Computational Linguistics (CL), Natural Language Processing (NLP), Information Retrieval (IR) and Formal Semantics — then do feel free to contact us.
This wiki is available under the domain name lac.essex.ac.uk.
Summer Term 2014
Tuesday, 17th June 2014: Research Group Meeting
Location and time: TBA, 12.00-2.00
Silviu Paun will give a dry run of the work he's going to present at PGNET
Tuesday, 3rd June 2014: Prof. Stephen Pulman (University of Oxford)
Compositional sentiment analysis
Location and time: 1N1.4.1, 12.00-2.00
Sentiment Analysis - recognising positive and negative attitudes expressed in text - has become a very popular application of computational linguistics techniques, spawning a large number of startups, and generating a lot of commercial interest. In this talk I summarise recent research and trends in sentiment analysis, look at some relatively novel applications, and also critically examine various claims that have been made about the role of sentiment analysis in tasks like stock market prediction and election result forecasting.
Speaker short Bio
Stephen Pulman is a Professorial Fellow of Somerville College, Oxford, and a Fellow of the British Academy. He has also held visiting professorships at the Institut für Maschinelle Sprachverarbeitung, University of Stuttgart; and at Copenhagen Business School. He is a co-founder of TheySay Ltd, a sentiment analysis company which was spun out of the department in 2010.
Tuesday, 20th May 2014: Research Group Meeting
Location and time: 3.501, 12.00-2.00
Richard Sutcliffe and Chris Fox (University of Essex)
C@MERATA at MediaEval 2014 - Extracting Answer Passages from Classical Music Scores using Natural Language Descriptions
When studying musicological analyses of works of western classical art music there are frequent references to relevant passages in the printed score. Indeed, musicologists can refer to very complex aspects of a score within a text. Other experts know what passages they are talking about because they can interpret musical terminology in an appropriate fashion and can then look through scores to find the passages in question. However, this can be time consuming. So the long-term aim here is to develop tools which could facilitate the work of musicologists.
In the C@merata task, there will be a series of questions with required answers. Each question will consist of a short noun phrase in English referring to musical features in a score and a short classical music score in MusicXML. The required answer will consist of zero or more passages occurring in the score which contain the musical features specified in the question. A passage consists of a start point and an end point in the score associated with the question.
This talk will present our work up to now on the C@merata Task, including details of the task and directions for the future.
More information about the project is available from the C@MERATA 14: Question Answering on Classical Music Scores page
Tuesday, 8th April 2014, CS Colloquium room, 12.00-2.00
The group met to discuss ideas about what to cover in the summer term and the frequency of meetings.
Wednesday, 26th February 2014: Bob Carpenter (Columbia University)
Probabilistic Models of Annotation
Room and Time: CS Colloquium room, 12.00-2.00
Standard agreement measures for inter-annotator reliability are neither
necessary nor sufficient to ensure a high quality corpus. Compared to
conventional agreement measures (e.g., kappa, alpha), probabilistic
annotation models provide more information about quantities of interest
including gold standard labels, sense prevalence, and individual
annotator accuracies and biases.
I will present a large scale, open access, case study of word sense
annotation using examples drawn across genres from the American National
Corpus (ANC) with WordNet word senses. I will contrast in-house trained
and supervised annotators with crowdsourced annotations gathered using
Amazon's Mechanical Turk.
I will conclude with an application of mutual information (i.e., expected
information gain) to measure the value of gathering a label from an
This is joint work with Becky Passonneau.
Thursday, 6th February: Gareth J. F. Jones, CNGL: Centre for Global Intelligent Content, School of Computing, Dublin City University, Ireland
Utilizing Recommender Algorithms for Enhanced Information Retrieval
Date and time: 10-12, Room 3.411
Retrieving relevant items which meet a user’s information need is the
key objective of information retrieval (IR). Current IR systems
generally seek to satisfy search queries independently without
considering search history information from other searchers. By
contrast, algorithms used in recommender systems (RSs) are designed to
predict the future popularity of an item by aggregating ratings of the
reactions of previous users of an item. This observation motivates us to
explore the application of RS methods in IR to increase search
effectiveness. In this study, we examine the suitability of recommender
algorithms (RAs) for use in IR applications and methods for combining
RAs into IR systems by fusing their respective outputs. Novel methods
are introduced including an adapted RA to enhance performance of RSs in
our integrated application, and an approach using cluster-based link
analysis. Experimental results are reported for an extended version of
the FIRE 2011 personalized IR data collection.
(joint work with Wei Li)
Autumn Term 2013
This term we looked at big data computation with Hadoop and other tools.
Language and Computation Day
Monday, 30th of September 2013, all day — 1N1.4.1
Find details here.