Oren Tsur
ABSTRACT
We apply machine learning techniques to study language transfer, a major topic in the theory of Second Language Acquisition (SLA). Using an SVM for the problem of native language classification, we show that a careful analysis of the effects of various features can lead to scientific insights. In particular, we demonstrate that character bigrams alone allow classification levels of about 66% for a 5-class task, even when content and function word differences are accounted for. This may show that native language has a strong effect on the word choice of people writing in a second language.
- Argamon S., Koppel M. and Shimoni A. 2003. Gender, Genre, and Writing Style in Formal Written Texts. Text 23(3).Google Scholar
- Bouselmi G., Fohr D., Illina, I., and Haton J. P. 2005. Fully Automated Non-Native Speech Recognition Using Confusion-Based Acoustic Model. Eurospeech/Interspeech '05.Google Scholar
- Bouselmi G., Fohr D., Illina I., and Haton J. P. 2006. Fully Automated Non-Native Speech Recognition Using Confusion-Based Acoustic Model Integration and Graphemic Constraints. IEEE International Conference on Acoustics, Speech and Signal Processing, 2006.Google Scholar
- Bybee J. 2006. Frequency of Use and the Organization of Language. Oxford University Press.Google Scholar
- Clark, E. 2003. First Language Acquisition. Cambridge University Press.Google Scholar
- Diederich J., Kindermann J., Leopold E. and Paass G. 2004. Authorship Attribution with Support Vector Machines. Applied Intelligence, 109--123. Google ScholarDigital Library
- Ellis N. 2002. Frequency Effects in Language Processing. Studies in Second Language Acquisition, 24(2):143--188.Google ScholarCross Ref
- Ellis R. 1999. Understanding Second Language Acquisition. Oxford University Press.Google Scholar
- Granger S., Dagneaux E. and Meunier F. 2002. International Corpus of Learner English. Presses universitaires de Louvain.Google Scholar
- Hansen J. H., Yapanel U., Huang, R. and Ikeno A. 2004. Dialect Analysis and Modeling for Automatic Classification. Interspeech-2004/ICSLP-2004: International Conference Spoken Language Processing. Jeju Island, South Korea.Google Scholar
- Holmes D. and Forsyth R. 1995. The Federalist Revisited: New Directions in Authorship Attribution. Literary and Linguistic Computing, pp. 111--127.Google Scholar
- James C. E. 1980. Contrastive Analysis. New York: Longman.Google Scholar
- Jusczyk P. W. 1997. The Discovery of Spoken Language. MIT Press.Google Scholar
- Koppel M. and Schler J. 2003. Exploiting Stylistic Idiosyncrasies for Authorship Attribution. In Proceedings of IJCAI '03 Workshop on Computational Approaches to Style Analysis and Synthesis. Acapulco, Mexico.Google Scholar
- Koppel M., Schler J. and Zigdon K. 2005(a). Determining an Author's Native Language by Mining a Text for Errors. Proceedings of KDD '05. Chicago IL. Google ScholarDigital Library
- Koppel M., Schler J. and Zigdon K. 2005(b). Automatically Determining an Anonymous Author's Native Language. In Intelligence and Security Informatics (pp. 209--217). Berlin / Heidelberg: Springer. Google ScholarDigital Library
- Odlin T. 1989. Language Transfer: Cross-Linguistic Influence in Language Learning. Cambridge University Press.Google ScholarCross Ref
- Porter F. M. 1980. An Algorithm for Suffix Stripping. Program, 14(3):130--137.Google ScholarCross Ref
- Saffran J. R. 2001. Words in a Sea of Sounds: The Output of Statistical Learning. Cognition, 81, 149--169.Google Scholar
- Saffran J. R. 2002. Constraints on Statistical Language Learning. Journal of Memory and Language, 47, 172--196.Google ScholarCross Ref
- Saffran J. R., Aslin R. N. and Newport E. N. 1996. Statistical Learning by 8-month Old Infants. Science, issue 5294, 1926--1928.Google Scholar
- Salton G. and Buckley C. 1988. Term Weighing Approaches in Automatic Text Retrieval. Information Processing and Management, 24(5):513--523. Google ScholarDigital Library
- Schölkopf B,. Smola A 2002. Learning with Kernels. MIT Press.Google Scholar
- Stamatatos E,. Fakotakis N. and Kokkinakis G. 2004. Computer-Based Authorship Attribution Without Lexical Measures. Computers and the Humanities, 193--214.Google Scholar
- Witten I. H. and Frank E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann. Google ScholarDigital Library
- Yang C. 2004. Universal Grammar, Statistics, or Both?. Trends in Cognitive Science 8(10):451--456, 2004.Google ScholarCross Ref
- Using classifier features for studying the effect of native language on the choice of written second language words
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