Language Technology Seminar Series
Title: Application of Hidden Markov Models for Semantic Role Labelling
Speaker: Phil Blunsom
Location: ICT Building, L2.06
Date: Friday 18 June 2004
The Hidden Markov Model (HMM) is a popular statistical tool for
modelling a wide range of time series data. In the context of NLP, HMMs
have been applied with great success to problems such as part-of-speech
tagging and noun phrase chunking. In this seminar I will introduce and
define HMMs and describe efficient methods for addressing the three
classic tasks of evaluation, decoding and learning. I will then
describe the application of HMMs to the task of Semantic Role Labelling
(SRL) and compare my models with those presented in this years
Conference on Computational Natural Language Learning (CoNLL) Shared
Task. SRL is concerned with identifying and labelling the relationship
between a syntactic constituent and a verb predicate, typical semantic
arguments are Agent, Patient etc. SRL is a key task for generalising
Information Extraction systems beyond task specific and hand coded
template filling methods.
Phil Blunsom is a Master's student in the Language Technology Group in CSSE. His research
is in the area of statistical models for semantic role annotation.
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