Abstract
Word embedding, which encodes words into vectors, is an important starting point in natural language processing and commonly used in many text-based machine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a novel neighbor embedding method which directly learns an embedding simplex where the similarities between the mapped words are optimal in terms of minimal discrepancy to the input neighborhoods. Our method is built upon two-step random walks between words via topics and thus able to better reveal the topics among the words. Experiment results indicate that our method, compared with another existing word embedding approach, is more favorable for various queries.
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References
Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G.E., Hinton, G.E.: Large scale distributed neural network training through online distillation. In: International Conference on Learning Representations (2018)
Dikmen, O., Yang, Z., Oja, E.: Learning the information divergence. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1442–1454 (2015)
Dingwall, N., Potts, C.: Mittens: an extension of glove for learning domain-specialized representations. In: NAACL-HLT, pp. 212–217 (2018)
Hunter, D., Lange, K.: A tutorial on MM algorithms. Am. Stat. 58(1), 30–37 (2004)
Ling, W., Dyer, C., Black, A., Trancoso, I.: Two/too simple adaptations of Word2Vec for syntax problems. In: NAACL-HLT, pp. 1299–1304 (2015)
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Merity, S., Xiong, C., Bradbury, J., Socher, R.: Pointer sentinel mixture models. In: International Conference on Learning Representations (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Sinkkonen, J., Aukia, J., Kaski, S.: Component Models for Large Networks. CoRR abs/0803.1628 (2008)
Stamatatos, E., Kokkinakis, G., Fakotakis, N.: Automatic text categorization in terms of genre and author. Comput. Linguist. 26(4), 471–495 (2000)
Stergiou, S., Straznickas, Z., Wu, R., Tsioutsiouliklis, K.: Distributed negative sampling for word embeddings. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 2569–2575 (2017)
Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394 (2010)
Yang, Z., Corander, J., Oja, E.: Low-rank doubly stochastic matrix decomposition for cluster analysis. J. Mach. Learn. Res. 17(187), 1–25 (2016)
Yang, Z., Oja, E.: Unified development of multiplicative algorithms for linear and quadratic nonnegative matrix factorization. IEEE Trans. Neural Netw. 22(12), 1878–1891 (2011)
Yang, Z., Peltonen, J., Kaski, S.: Majorization-minimization for manifold embedding. In: International Conference on Artificial Intelligence and Statistics, pp. 1088–1097 (2015)
Yang, Z., Zhu, Z., Oja, E.: Automatic rank determination in projective nonnegative matrix factorization. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 514–521. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15995-4_64
Zhu, Z., Yang, Z., Oja, E.: Multiplicative updates for learning with stochastic matrices. In: Kämäräinen, J.-K., Koskela, M. (eds.) SCIA 2013. LNCS, vol. 7944, pp. 143–152. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38886-6_14
Acknowledgment
The work is supported by Finnish Academy (grant numbers 307929 and 314177) and the Telenor-NTNU AI Lab project.
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Sedov, D., Yang, Z. (2018). Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decomposition. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_9
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DOI: https://doi.org/10.1007/978-3-030-04182-3_9
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