BScAdv(Hons) Sydney, PhD Edinburgh
Associate Professor and ARC Australian Research Fellow
SIT Building J12, Room
(Cnr Cleveland St and City Rd)
P: +61 2
F: +61 2 9351 3838
AT sydney DOT
edu DOT au
My research is in computational linguistics, focusing on robust statistical approaches to
language processing (NLP). My interests range from the
design of fundamental NLP components, including text processing and tagging tools,
through to statistical parsers and high
level systems for financial modelling using text,
question answering and info
My background is in computer science. Computational linguistics poses challenges that
require both algorithmic and implementation techniques of interest to computer scientists.
Meanwhile, computational linguists are developing complex f
ormalisms and statistical
models that enable increasingly detailed linguistic analyses. Unfortunately, greater fidelity
usually brings significant efficiency penalties. Providing even a superficial analysis of the
rapidly growing volume of text now availab
le is a prodigious task.
I am excited by the challenges of developing large
scale and robust deep
processing techniques that are feasible for tera
scale datasets. I believe that statistical
parsing with lexicalised grammar formalisms, e.g. Combi
natory Categorial Grammar (CCG),
and supertagging, provides the best trade
off between linguistic fidelity and efficiency.
Efficient, accurate parsing will enable us to create and exploit unprecedented quantities of
automatically analysed text using semi
upervised knowledge acquisition. This will be crucial
to overcoming the knowledge bottleneck that hampers real
world applications of NLP.
The following list is a selection from publications (
J Nothman, N Ringland, W Radfor
d, T Murphy, and J R
Curran (2012). Learning multilingual
named entity recognition from Wikipedia. Artificial Intelligence, Elsevier.
B Hachey, W Radford, J Nothman, M Honnibal, and J R
Curran (2012). Evaluating Entity
Linking with Wikipedia.
Artificial Intelligence, Elsevier.
D Vadas and J R
Curran (2011). Parsing noun phrases in the Penn Treebank.
Computational Linguistics, 37(4). MIT Press.
and J R
Curran (2007). Wide
Coverage Efficient Statistical Parsing with CCG and
Models. Computational Linguistics 33(4):493
J R Curran, T
Murphy, and B. Scholz (2007). Minimising semantic drift with Mutual Exclusion
Bootstrapping. In Proceeding. of the Conference of the Pacific Association for Computational
Linguistics, pp 172
180. Melbourne, Australia. Best Paper Award.
J Gorman and J R
Curran (2006) Scaling Distributional Similarity to Large Corpora. In Proc.
of the 21st International Conference on Computational Linguistics and 44th Annual Meeting
of the Association for Comput
ational Linguistics, pp 361
368, Sydney, Australia.
Clark and J R Curran (2004) Parsing the WSJ using CCG and Log
Linear Models. In
Proceeding. of the 42nd Annual Meeting of the Association for Computational Linguistics
(ACL), pp 104
111, Barcelona, S
S Clark and J R
Curran (2004) The Importance of Supertagging for Wide
Parsing. In Proceeding. of the 20th International Conference on Computational Linguistics
(COLING), pp 282
288, Geneva, Switzerland.
J R Curran and S
Clark (2003) I
nvestigating GIS and Smoothing for Maximum Entropy
Taggers. In Proceeding. of the 11th Conference of the European Chapter of the Association
for Computational Linguistics (EACL), pp 91
98, Budapest, Hungary.
J R Curran and S
Clark (2003) Language Independent NER using a Maximum Entropy
Tagger. In Proceeding. of the 7th Conference of Natural Language Learning (CoNLL), pp
167, Edmonton, Canada.
Introductory and advanced programming, data structures, alg
engineering, artificial intelligence, machine learning, computational linguistics.
INFO1903: Informatics (Advanced)
ENGG1801: Engineering Computing
COMP5046: Statistical Natural Language Processing