Abstract
We introduce a method to automatically produce large analogical clusters from smaller seed clusters of representative examples. The method is based on techniques of processing and solving analogical equations in word vector space models, i.e., word embeddings. In our experiments, we use standard data sets in English which cover different relations extending from derivational morphology (like adjective–adverb, positive–comparative forms of adjectives) or inflectional morphology (like present–past forms) to encyclopedic semantics (like country–capital relations). The analogical clusters produced by our method are shown to be of reasonably good quality, as shown by comparing human judgment against automatic NDCG@n scores. In total, they contain 8.5 times as many relevant word pairs as the seed clusters.
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
Notes
- 1.
We thank anonymous reviewers for pointing out these issues of our current method.
References
Bach, N., Badaskar, S.: A review of relation extraction. Lit. Rev. Lang. Stat. II 2 (2007)
Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, ICML 2005, pp. 89–96. ACM, New York (2005). https://doi.org/10.1145/1102351.1102363
Chklovski, T.: Learner: a system for acquiring commonsense knowledge by analogy. In: Proceedings of the 2nd International Conference on Knowledge Capture, pp. 4–12. ACM (2003)
Drozd, A., Gladkova, A., Matsuoka, S.: Word embeddings, analogies, and machine learning: beyond king - man + woman = queen. In: Proceedings of COLING 2016, pp. 3519–3530 (2016)
Gentner, D.: Structure-mapping: a theoretical framework for analogy. Cogn. Sci. 7(2), 155–170 (1983)
Gladkova, A., Drozd, A., Matsuoka, S.: Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. In: Proceedings of the NAACL Student Research Workshop, pp. 8–15 (2016)
Hendrickx, I., et al.: Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, DEW 2009, pp. 94–99. Association for Computational Linguistics, Stroudsburg (2009). http://dl.acm.org/citation.cfm?id=1621969.1621986
Holyoak, K.J., Holyoak, K.J., Thagard, P.: Mental Leaps: Analogy in Creative Thought. MIT Press, Cambridge (1996)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002). https://doi.org/10.1145/582415.582418
Lepage, Y.: Analogies between binary images: application to chinese characters. In: Prade, H., Richard, G. (eds.) Computational Approaches to Analogical Reasoning: Current Trends. SCI, vol. 548, pp. 25–57. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54516-0_2
Levy, O., Goldberg, Y.: Linguistic regularities in sparse and explicit word representations. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning (CoNLL2014), pp. 171–180 (2014)
Linzen, T.: Issues in evaluating semantic spaces using word analogies. In: Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, pp. 13–18. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/W16-2503. http://www.aclweb.org/anthology/W16-2503
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP2014), pp. 1532–1543 (2014)
Turney, P.D.: Measuring semantic similarity by latent relational analysis. arXiv preprint arXiv:cs/0508053 (2005)
Turney, P.D.: A uniform approach to analogies, synonyms, antonyms, and associations. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 905–912. Association for Computational Linguistics (2008)
Veale, T., Keane, M.T.: The competence of sub-optimal structure mapping on ‘hard’ analogies. The proceedings of IJCAI’97, the Int. In: Joint Conference on Artificial Intelligence, Nagoya, Japan. Morgan Kaufman, San Mateo California (1997)
Veale, T., Li, G.: Analogy as an organizational principle in the construction of large knowledge-bases. In: Prade, H., Richard, G. (eds.) Computational Approaches to Analogical Reasoning: Current Trends. SCI, vol. 548, pp. 83–101. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54516-0_4
Wang, H., Yang, W., Lepage, Y.: Sentence generation by analogy: towards the construction of a quasi-parallel corpus for Chinese-Japanese. In: Proceedings of the 20th Annual Meeting of the Japanese Association for Natural Language Processing, Sapporo, pp. 900–903, March 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hong, Y., Lepage, Y. (2018). Production of Large Analogical Clusters from Smaller Example Seed Clusters Using Word Embeddings. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-01081-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01080-5
Online ISBN: 978-3-030-01081-2
eBook Packages: Computer ScienceComputer Science (R0)