Abstract
The use of distributed representations, e.g., via word2vec, has become popular in recent years. However, Japanese has many compound words and we often face the situation where meanings of a word and a compound word should be compared. Therefore, in the current study, we composed compound word vectors from those of constituent word vectors. We took into consideration the dependency relations of compound words to compose word vectors of them. The experiments revealed that, when we consider dependency relations, (1) we could obtain better representations for compound words when we separately learn models for each dependency relation, (2) each model could obtain good representations with fewer epochs, and (3) the learned weights for a model of compound words with one dependency relation could be used for fine-tuning for models for compound words of other dependency relations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
http://unidic.ninjal.ac.jp/(In Japanese).
- 2.
means strawberries, - 3.
The relations that had less than 30 examples were stuck in‘others’ class.
- 4.
“
- 5.
- 6.
means strawberries,References
Baroni, M., Zamparelli, R.: Nouns are vectors, adjectives are matrices: representing adjective-noun constructions in semantic space. In: Proceedings of EMNLP 2010, pp. 1183–1193 (2010)
Hashimoto, K., Tsuruoka, Y.: Learning embeddings for transitive verb disambiguation by implicit tensor factorization. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pp. 1–11 (2015)
Hashimoto, K., Tsuruoka, Y.: Adaptive joint learning of compositional and non-compositional phrase embeddings. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 205–215. Association for Computational Linguistics (2016). https://arxiv.org/abs/1603.06067
Maekawa, K., et al.: Design, compilation, and preliminary analyses of balanced corpus of contemporary written Japanese. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), pp. 1483–1486 (2010)
Maekawa, K., et al.: Balanced corpus of contemporary written Japanese. In: Language Resources and Evaluation, vol. 48, pp. 345–371 (2014)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of ICLR Workshop 2013, pp. 1–12 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS 2013, pp. 1–9 (2013)
Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL 2013, pp. 746–751 (2013)
Muraoka, M., Shimaoka, S., Yamamoto, K., Watanabe, Y., Okazaki, N., Inui, K.: Finding the best model among representative compositional models. In: Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation (PACLIC 2014), pp. 65–74 (2014)
Socher, R., Bauer, J., Manning, C.D., Ng, A.Y.: Parsing with compositional vector grammars. In: Proceedings of ACL 2013, pp. 455–465 (2013)
Socher, R., Karpathy, A., Le, Q.V., Manning, C.D., Ng, A.Y.: Grounded compositional semantics for finding and describing images with sentences. Trans. Assoc. Comput. Linguistics 2, 207–218 (2014)
Acknowledgments
This work was partially supported by JSPS KAKENHI Grant Number 18K11421 and research grant of Woman Empowerment Support System of Ibaraki University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Komiya, K., Seitou, T., Sasaki, M., Shinnou, H. (2023). Composing Word Vectors for Japanese Compound Words Using Dependency Relations. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-24337-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24336-3
Online ISBN: 978-3-031-24337-0
eBook Packages: Computer ScienceComputer Science (R0)