Contact
[my initials] @berkeley.edu
Where my initials are 'jkk'
Mailing Address:
7th floor Sutardja Dai Hall, Desk 078
Berkeley, CA 94720-1776
About Me
I am a Monash Scholar, currently studying for a PhD in Computer Science at UC Berkeley.
My research focus is Natural Language Processing — in particular, the task of coreference resolution.
I am a member of the Berkeley NLP Group, and my advisor is Dan Klein.
I graduated from the University of Sydney with a Bachelor of Science (Advanced) with First Class Honours and Medal in Computer Science in 2009.
For my honours thesis, "Adaptive Supertagging for Faster Parsing", I studied in the Schwa lab, advised by James Curran.
I completed my Higher School Certificate at Emanuel School, receiving the Premier's Award for my results in English, Mathematics, Physics and Cosmology.
Selected Publications
Faster Parsing by Supertagger Adaptation
Jonathan K. Kummerfeld, Jessica Roesner, Tim Dawborn, James Haggerty, James R. Curran and Stephen Clark
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, July 2010, pages 345–355
[paper]
[bib]
[other info]
@InProceedings{Kummerfeld-Roesner-Dawborn-Haggerty-Curran-Clark:2010:ACL,
title = {Faster Parsing by Supertagger Adaptation},
author = {Jonathan K. Kummerfeld and Jessica Roesner and Tim Dawborn and James Haggerty and James R. Curran and Stephen Clark},
booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2010},
address = {Uppsala, Sweden},
publisher = {Association for Computational Linguistics},
pages = {345--355},
url = {http://www.aclweb.org/anthology/P/P10/P10-1036.pdf},
abstract = {We propose a novel self-training method for a parser which uses a lexicalised grammar and supertagger, focusing on increasing the speed of the parser rather than its accuracy. The idea is to train the supertagger on large amounts of parser output, so that the supertagger can learn to supply the supertags that the parser will eventually choose as part of the highest scoring derivation. Since the supertagger supplies fewer supertags overall, the parsing speed is increased. We demonstrate the effectiveness of the method using a CCG supertagger and parser, obtaining significant speed increases on newspaper text with no loss in accuracy. We also show that the method can be used to adapt the CCG parser to new domains, obtaining accuracy and speed improvements for Wikipedia and biomedical text.},
}
bib as a file
The densest packing of AB binary hard-sphere homogeneous compounds across all size ratios
Jonathan K Kummerfeld, Toby S Hudson and Peter Harrowell
The Journal of Physical Chemistry B, August 2008, pages 10773–10776
[paper]
[bib]
[other info]
@Article{Kummerfeld-Hudson-Harrowell:2008:JPhysChemB,
title = {The densest packing of AB binary hard-sphere homogeneous compounds across all size ratios},
author = {Jonathan K Kummerfeld and Toby S Hudson and Peter Harrowell},
journal = {The Journal of Physical Chemistry B},
month = {August},
year = {2008},
volume = {112},
issue = {35},
pages = {10773--10776},
url = {http://pubs.acs.org/doi/abs/10.1021/jp804953r},
abstract = {This paper considers the homogeneous packing of binary hard spheres in an equimolar stoichiometry, and postulates the densest packing at each sphere size ratio. Monte Carlo simulated annealing optimizations are seeded with all known atomic inorganic crystal structures, and the search is performed within the degrees of freedom associated with each homogeneous AB structure type. Structures isopointal to the FeB structure type are found to have the highest packing fraction at all sphere size ratios. The optimized structures match or improve on the best previously demonstrated packings of this type, and show that compound structures can pack more densely than segregated close-packed structures at all radius ratios less than 0.62.},
}
bib as a file
Classification of Verb Particle Constructions with the Google Web1T Corpus
Jonathan K. Kummerfeld and James R. Curran
Proceedings of the Australasian Language Technology Association Workshop 2008, December 2008, pages 55–63
[paper]
[bib]
[other info]
@InProceedings{Kummerfeld-Curran:2008:ALTA,
title = {Classification of Verb Particle Constructions with the Google Web1T Corpus},
author = {Jonathan K. Kummerfeld and James R. Curran},
booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2008},
month = {December},
year = {2008},
address = {Hobart, Australia},
pages = {55--63},
volume = {6},
url = {http://www.aclweb.org/anthology/U/U08/U08-1008.pdf},
abstract = {Manually maintaining comprehensive databases of multi-word expressions, for example Verb-Particle Constructions (VPCs), is infeasible. We describe a new classifier for potential VPCs, which uses information in the Google Web1T corpus to perform a simple linguistic constituency test. Specifically, we consider the fronting test, comparing the frequencies of the two possible orderings of the given verb and particle. Using only a small set of queries for each verb-particle pair, the system was able to achieve an F-score of 78.4% in our evaluation while processing thousands of queries a second.},
}
bib as a file
Bibtex can be found on my publications page.