Korean L2 vocabulary prediction: Can a large annotated corpus be used to train better models for predicting unknown words?

Kevin P. Yancey, Yves Lepage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Vocabulary knowledge prediction is an important task in lexical text simplification for foreign language learners (L2 learners). However, previously studied methods that use hand-crafted rules based on one or two word features have had limited success. A recent study hypothesized that a supervised learning classifier trained on a large annotated corpus of words unknown by L2 learners may yield better results. Our study crowdsourced the production of such a corpus for Korean, now consisting of 2,385 annotated passages contributed by 357 distinct L2 learners. Our preliminary evaluation of models trained on this corpus show favorable results, thus confirming the hypothesis. In this paper, we describe our methodology for building this resource in detail and analyze its results so that it can be duplicated for other languages. We also present our preliminary evaluation of models trained on this annotated corpus, the best of which recalls 80 % of unknown words with 71 % precision. We make our annotation data available.

Original languageEnglish
Title of host publicationLREC 2018 - 11th International Conference on Language Resources and Evaluation
EditorsHitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga
PublisherEuropean Language Resources Association (ELRA)
Pages438-445
Number of pages8
ISBN (Electronic)9791095546009
Publication statusPublished - 2019 Jan 1
Event11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan
Duration: 2018 May 72018 May 12

Other

Other11th International Conference on Language Resources and Evaluation, LREC 2018
CountryJapan
CityMiyazaki
Period18/5/718/5/12

Keywords

  • Crowdsourcing
  • Lexical simplification
  • Vocabulary knowledge prediction

ASJC Scopus subject areas

  • Linguistics and Language
  • Education
  • Library and Information Sciences
  • Language and Linguistics

Fingerprint Dive into the research topics of 'Korean L2 vocabulary prediction: Can a large annotated corpus be used to train better models for predicting unknown words?'. Together they form a unique fingerprint.

Cite this