Cover not available
Part of
Recent Advances in Multiword Units in Machine Translation and Translation Technology
Edited by Johanna Monti, Gloria Corpas Pastor, Ruslan Mitkov and Carlos Manuel Hidalgo-Ternero
[Current Issues in Linguistic Theory 366] 2024
► pp. 219–243

Chapter 12
A comprehensive Japanese MWE lexicon

JMWEL

| Kurume Institute of Technology, emeritus, Japan
| Fukuoka University, Japan
| CJKI Co., Japan
| Fukuoka University, emeritus, Japan
JMWEL (Japanese MWE Lexicon) is a comprehensive lexicon of Japanese Multiword Expressions (MWEs) with a rich set of grammatical attributes fine-tuned for phrase-based processing of a wide range of Japanese documents. It has about 160,000 MWE lemmas covering almost every kind of linguistically idiosyncratic but commonly used Japanese phrases, e.g., idioms, quasi-idioms, collocations, quasi-collocations, clichés, quasi-clichés, institutionalized phrases, proverbs, and old sayings, excepting technical terms in specialized fields or named entities. JMWEL consists of sixteen sub-lexicons reflecting their distinctive features. The comprehensiveness of the collected MWEs and the detailed morpho-syntactic information given to each MWE, which may include internal modifiers, are notable features of JMWEL. In this paper, we introduce the newest version of JMWEL.
Keywords: multiword expression (MWE), phrase-based NLP, phrase-based machine translation (PBMT), phrase-based statistical machine translation (PBSMT), neural machine translation (NMT), lexical bundles, formulaic language, construction grammar, phraseology
Article outline
  • 1.Introduction
  • 2.MWE lemma
    • 2.1Non-compositionality
    • 2.2Probabilistically idiosyncratic MWEs
    • 2.3Distribution of MWEs in JMWEL
  • 3.Organization of JMWEL
    • 3.1Sub-lexicons compiled based on grammatical functions
    • 3.2Sub-lexicons organized by topic
  • 4.Data entries in JMWEL
    • 4.1Type label
    • 4.2Lemma
    • 4.3Constituent morphemes
    • 4.4Orthographic variants
    • 4.5Syntactic function
    • 4.6Morpho-syntactic structure
    • 4.7Internal modification
    • 4.8Forward context condition
    • 4.9Backward context condition
    • 4.10Inflection
    • 4.11Interpretation
  • 5.Applications
  • 6.Related work
  • 7.Concluding remarks
  • Acknowledgements
  • Notes
  • References
This content is being prepared for publication; it may be subject to changes.
References (16)
References
Biber, D., Johansson, S., Leech, G., Conrad, S., & Finegan, E. (Eds.) (1999). Longman grammar of spoken and written English. Pearson Education.Google Scholar
Church, K. (2011). How many multiword expressions do people know? Proceedings of the Workshop on Multiword Expressions: From Parsing and Generation to the Real World (MWE 2011), (pp. 137–144). Invited Talk.Google Scholar
Constant, M., Eryigit, G., Monti, J., van der Plas, L., Ramisch, C., Rosner, M., & Todirascu, A. (2017). Multiword expression processing: A survey. Computational Linguistics, 43(4), 837–892. DOI logoGoogle Scholar
Corrigan, R., Moravcsik, E. A., Ouali, H., & Wheatley, K. (Eds.) (2009). Formulaic language, vol.1 distribution and historical change. John Benjamins. DOI logoGoogle Scholar
Cowie, A. P. (Ed.) (1998). Phraseology: Theory, analysis, and applications. (Oxford Studies in Lexicography and Lexicology). Clarendon Press. DOI logoGoogle Scholar
Fillmore, C., Kay, P., & O’Connor, M. C. (1988). Regularity and idiomatical grammatical construction: The case of let alone. Language, 64, 501–538. DOI logoGoogle Scholar
Hashimoto, C., & Kawahara, D. (2008). Construction of an idiom corpus and its application to idiom identification based on WSD incorporating idiom-specific features. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 992–1001). DOI logo
Kudo, T., & Kazawa, H. (2009). Japanese web n-gram Version 1. Linguistic Data Consortium 2009. T08.
Ramisch, C. (2017). Putting the Horses Before the Cart: Identifying Multiword Expressions Before Translation. The EUROPHRAS 2017 (MUMTTT 2017). Proceedings (pp. 69–84). Invited Talk. London. DOI logoGoogle Scholar
Sag, I. A., Baldwin, T., Bond, F., Copestake, A., & Flickinger, D. (2002). Multiword expressions: A pain in the neck for NLP. Proceedings of the 3rd International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2002) (pp. 1–15).
Savary, A., Cordeiro, S. R., & Ramisch, C. (2019). Without lexicons, multiword expression identification will never fly: A position statement. Proceedings of EUROPHRAS 2019 (pp. 79–91).
Shudo, K. (2011). Research studio for Japanese language processing, [URL]
, (1973). On machine translation from Japanese into English for a technical field. Journal of the Information Processing Society of Japan, 14(9), 661–668. (Japanese title: 専門分野を対象とした日英機械翻訳について).Google Scholar
Shudo, K., Fujita, T., & Yoshida, S. (1978). On the processing of annexational expressions in Japanese. Proceedings of the international conference on the Computational Linguistics, COLING78 (pp. 53.1–53.11).
Tanabe, T, Takahashi, M., & Shudo, K. (2014). A lexicon of multiword expressions for linguistically precise, wide-coverage natural language processing. Computer Speech and Language, 28(6), 1317–1339. Elsevier. DOI logoGoogle Scholar
Zaninello, A., & Birch, A. (2020). Multiword expression aware neural machine translation. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) (pp. 3816–3825).