---
abstract: |-
Existing statistical approaches to natural language problems are very
coarse approximations to the true complexity of language processing.
As such, no single technique will be best for all problem instances.
Many researchers are examining ensemble methods that combine the
output of successful, separately developed modules to create more
accurate solutions. This paper examines three merging rules for
combining probability distributions: the well known mixture rule, the
logarithmic rule, and a novel product rule. These rules were applied
with state-of-the-art results to two problems commonly used to assess
human mastery of lexical semantics -- synonym questions and analogy
questions. All three merging rules result in ensembles that are more
accurate than any of their component modules. The differences among the
three rules are not statistically significant, but it is suggestive
that the popular mixture rule is not the best rule for either of the
two problems.
altloc: []
chapter: ~
commentary: ~
commref: ~
confdates: 10-12 September 2003
conference: International Conference on Recent Advances in Natural Language Processing (RANLP-03)
confloc: 'Borovets, Bulgaria'
contact_email: ~
creators_id: []
creators_name:
- family: Turney
given: Peter
honourific: ''
lineage: ''
- family: Littman
given: Michael
honourific: ''
lineage: ''
- family: Bigham
given: Jeffrey
honourific: ''
lineage: ''
- family: Shnayder
given: Victor
honourific: ''
lineage: ''
date: 2003
date_type: published
datestamp: 2003-09-19
department: ~
dir: disk0/00/00/31/63
edit_lock_since: ~
edit_lock_until: ~
edit_lock_user: ~
editors_id: []
editors_name: []
eprint_status: archive
eprintid: 3163
fileinfo: /style/images/fileicons/application_pdf.png;/3163/1/ranlp%2D03%2Dfinal%2Dversion.pdf
full_text_status: public
importid: ~
institution: ~
isbn: ~
ispublished: pub
issn: ~
item_issues_comment: []
item_issues_count: 0
item_issues_description: []
item_issues_id: []
item_issues_reported_by: []
item_issues_resolved_by: []
item_issues_status: []
item_issues_timestamp: []
item_issues_type: []
keywords: ~
lastmod: 2011-03-11 08:55:20
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ~
pagerange: 482-489
pubdom: FALSE
publication: ~
publisher: ~
refereed: TRUE
referencetext: |-
Eric Brill and Jun Wu. Classifier combination for improved
lexical disambiguation. In Proceedings of the Annual
Meeting of the Association for Computational Linguistics,
volume 1, pages 191-195, 1998.
David J. Chalmers, Robert M. French, and Douglas R.
Hofstadter. High-level perception, representation, and
analogy: a critique of artificial intelligence methodology.
Journal of Experimental and Theoretical Artificial
Intelligence, 4:185-211, 1992.
Cathy Claman. 10 Real SATs. College Entrance Examination
Board, 2000.
Christiane Fellbaum. WordNet: An Electronic Lexical
Database. The MIT Press, 1998.
Radu Florian and David Yarowsky. Modeling consensus:
Classifier combination for word sense disambiguation. In
Proceedings of the 2002 Conference on Empirical Methods in
Natural Language Processing (EMNLP 2002),
pages 25-32, 2002.
Robert M. French. The computational modeling of
analogy-making. Trends in Cognitive Sciences, 6(5):
200-205, 2002.
Tom Heskes. Selecting weighting factors in logarithmic
opinion pools. In Advances in Neural Information Processing
Systems, 10, pages 266-272, 1998.
Geoffrey E. Hinton. Products of experts. In Proceedings of
the Ninth International Conference on Artificial Neural
Networks (ICANN 99), volume 1, pages 1-6, 1999.
Robert A. Jacobs. Methods for combining experts' probability
assessments. Neural Computation, 7(5):867-888,
1995.
Robert A. Jacobs, Michael I. Jordan, Steve J. Nowlan, and
Geoffrey E. Hinton. Adaptive mixtures of experts. Neural
Computation, 3:79-87, 1991.
Mario Jarmasz and Stan Szpakowicz. Roget's thesaurus
and semantic similarity. In Proceedings of the International
Conference on Recent Advances in Natural Language
Processing (RANLP-03), in press, 2003.
George Lakoff and Mark Johnson. Metaphors We Live By.
University of Chicago Press, 1980.
Thomas K. Landauer and Susan T. Dumais. A solution to
Plato's problem: The latent semantic analysis theory of
acquisition, induction and representation of knowledge.
Psychological Review, 104(2):211-240, 1997.
Michael L. Littman, Greg A. Keim, and Noam Shazeer.
A probabilistic approach to solving crossword puzzles.
Artificial Intelligence, 134(1-2):23-55, 2002.
Robert E. Schapire. A brief introduction to boosting. In
Proceedings of the Sixteenth International Joint Conference
on Artificial Intelligence, pages 1401{1406, 1999.
Egidio Terra and C. L. A. Clarke. Frequency estimates
for statistical word similarity measures. In Proceedings
of the Human Language Technology and North American
Chapter of Association of Computational Linguistics
Conference 2003 (HLT/NAACL 2003), pages 244-251, 2003.
Peter D. Turney. Mining the web for synonyms: PMI-IR
versus LSA on TOEFL. In Proceedings of the Twelfth
European Conference on Machine Learning (ECML-2001),
pages 491-502, 2001.
Peter D. Turney and Michael L. Littman. Learning analogies
and semantic relations. Technical Report ERB-1103,
National Research Council, Institute for Information
Technology, 2003.
Peter D. Turney and Michael L. Littman. Measuring praise
and criticism: Inference of semantic orientation from association.
ACM Transactions on Information Systems,
in press, 2003.
Lei Xu, Adam Krzyzak, and Ching Y. Suen. Methods of
combining multiple classifiers and their applications to
handwriting recognition. IEEE Transactions on Systems,
Man and Cybernetics, 22(3):418-435, 1992.
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:48:44
subjects:
- comp-sci-stat-model
- comp-sci-lang
- ling-comput
- ling-sem
- comp-sci-mach-learn
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: "Combining independent modules to solve multiple-choice synonym and analogy problems\n"
type: confpaper
userid: 2175
volume: ~