Title |
Statistical Identification of English Loanwords in Korean Using Automatically Generated Training Data |
Authors |
Kirk Baker and Chris Brew |
Abstract |
This paper describes an accurate, extensible method for automatically classifying unknown foreign words that requires minimal monolingual resources and no bilingual training data (which is often difficult to obtain for an arbitrary language pair). We use a small set of phonologically-based transliteration rules to generate a potentially unlimited amount of pseudo-data that can be used to train a classifier to distinguish etymological classes of actual words. We ran a series of experiments on identifying English loanwords in Korean, in order to explore the consequences of using pseudo-data in place of the original training data. Results show that a sufficient quantity of automatically generated training data, even produced by fairly low precision transliteration rules, can be used to train a classifier that performs within 0.3% of one trained on actual English loanwords (96% accuracy). |
Language |
Multiple languages |
Topics |
Acquisition, Machine Learning, Multilinguality, Language modelling |
Full paper |
Statistical Identification of English Loanwords in Korean Using Automatically Generated Training Data |
Slides |
Statistical Identification of English Loanwords in Korean Using Automatically Generated Training Data |
Bibtex |
@InProceedings{BAKER08.295,
author = {Kirk Baker and Chris Brew},
title = {Statistical Identification of English Loanwords in Korean Using Automatically Generated Training Data},
booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
year = {2008},
month = {may},
date = {28-30},
address = {Marrakech, Morocco},
editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
publisher = {European Language Resources Association (ELRA)},
isbn = {2-9517408-4-0},
note = {http://www.lrec-conf.org/proceedings/lrec2008/},
language = {english}
} |