抄録
This study creates a language dataset for lexical simplification based on Common European Framework of References for Languages (CEFR) levels (CEFR-LS). Lexical simplification has continued to be one of the important tasks for language learning and education. There are several language resources for lexical simplification that are available for generating rules and creating simplifiers using machine learning. However, these resources are not tailored to language education with word levels and lists of candidates tending to be subjective. Different from these, the present study constructs a CEFR-LS whose target and candidate words are assigned CEFR levels using CEFR-J wordlists and English Vocabulary Profile, and candidates are selected using an online thesaurus. Since CEFR is widely used around the world, using CEFR levels makes it possible to apply a simplification method based on our dataset to language education directly. CEFR-LS currently includes 406 targets and 4912 candidates. To evaluate the validity of CEFR-LS for machine learning, two basic models are employed for selecting candidates and the results are presented as a reference for future users of the dataset.
| 元の言語 | 英語 |
|---|---|
| ホスト出版物のタイトル | LREC 2018 - 11th International Conference on Language Resources and Evaluation |
| 編集者 | Hitoshi 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 |
| 出版者 | European Language Resources Association (ELRA) |
| ページ | 3254-3258 |
| ページ数 | 5 |
| ISBN（電子版） | 9791095546009 |
| 出版物ステータス | 出版済み - 1 1 2019 |
| イベント | 11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, 日本 継続期間: 5 7 2018 → 5 12 2018 |
出版物シリーズ
| 名前 | LREC 2018 - 11th International Conference on Language Resources and Evaluation |
|---|
会議
| 会議 | 11th International Conference on Language Resources and Evaluation, LREC 2018 |
|---|---|
| 国 | 日本 |
| 市 | Miyazaki |
| 期間 | 5/7/18 → 5/12/18 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Linguistics and Language
- Education
- Library and Information Sciences
- Language and Linguistics
これを引用
CEFR-based lexical simplification dataset. / Uchida, Satoru; Takada, Shohei; Arase, Yuki.
LREC 2018 - 11th International Conference on Language Resources and Evaluation. 版 / Hitoshi 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. European Language Resources Association (ELRA), 2019. p. 3254-3258 (LREC 2018 - 11th International Conference on Language Resources and Evaluation).研究成果: 著書/レポートタイプへの貢献 › 会議での発言
}
TY - GEN
T1 - CEFR-based lexical simplification dataset
AU - Uchida, Satoru
AU - Takada, Shohei
AU - Arase, Yuki
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This study creates a language dataset for lexical simplification based on Common European Framework of References for Languages (CEFR) levels (CEFR-LS). Lexical simplification has continued to be one of the important tasks for language learning and education. There are several language resources for lexical simplification that are available for generating rules and creating simplifiers using machine learning. However, these resources are not tailored to language education with word levels and lists of candidates tending to be subjective. Different from these, the present study constructs a CEFR-LS whose target and candidate words are assigned CEFR levels using CEFR-J wordlists and English Vocabulary Profile, and candidates are selected using an online thesaurus. Since CEFR is widely used around the world, using CEFR levels makes it possible to apply a simplification method based on our dataset to language education directly. CEFR-LS currently includes 406 targets and 4912 candidates. To evaluate the validity of CEFR-LS for machine learning, two basic models are employed for selecting candidates and the results are presented as a reference for future users of the dataset.
AB - This study creates a language dataset for lexical simplification based on Common European Framework of References for Languages (CEFR) levels (CEFR-LS). Lexical simplification has continued to be one of the important tasks for language learning and education. There are several language resources for lexical simplification that are available for generating rules and creating simplifiers using machine learning. However, these resources are not tailored to language education with word levels and lists of candidates tending to be subjective. Different from these, the present study constructs a CEFR-LS whose target and candidate words are assigned CEFR levels using CEFR-J wordlists and English Vocabulary Profile, and candidates are selected using an online thesaurus. Since CEFR is widely used around the world, using CEFR levels makes it possible to apply a simplification method based on our dataset to language education directly. CEFR-LS currently includes 406 targets and 4912 candidates. To evaluate the validity of CEFR-LS for machine learning, two basic models are employed for selecting candidates and the results are presented as a reference for future users of the dataset.
UR - http://www.scopus.com/inward/record.url?scp=85059901092&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059901092&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059901092
T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation
SP - 3254
EP - 3258
BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Piperidis, Stelios
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Hasida, Koiti
A2 - Mazo, Helene
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Mariani, Joseph
A2 - Moreno, Asuncion
A2 - Calzolari, Nicoletta
A2 - Odijk, Jan
A2 - Tokunaga, Takenobu
PB - European Language Resources Association (ELRA)
ER -