Reconstructing native language typology from foreign language usage

Yevgeni Berzak, Roi Reichart, Boris Katz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native language properties on linguistic performance in a foreign language. In this work we provide empirical evidence for this process in the form of a strong correlation between language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native languages. We leverage this finding to recover native language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target languages. Our method achieves 72.2% accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources.

Original languageEnglish
Title of host publicationCoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings
Pages21-29
Number of pages9
ISBN (Electronic)9781941643020
DOIs
StatePublished - 2014
Event18th Conference on Computational Natural Language Learning, CoNLL 2014 - Baltimore, United States
Duration: 26 Jun 201427 Jun 2014

Publication series

NameCoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings

Conference

Conference18th Conference on Computational Natural Language Learning, CoNLL 2014
Country/TerritoryUnited States
CityBaltimore
Period26/06/1427/06/14

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Artificial Intelligence
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'Reconstructing native language typology from foreign language usage'. Together they form a unique fingerprint.

Cite this