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Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
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First-Episode Psychotic Patients Showed Longitudinal Brain Changes Using fMRI With an Emotional Auditory Paradigm
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Handwriting recognition by using deep learning to extract meaningful features
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Towards a Universal Semantic Dictionary
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Abstract:
[EN] A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, multilingual embedding space, is proposed in this paper. Previous approaches learned translation matrices between two specific languages, while this method learns translation matrices between a given language and a shared, multilingual space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case, two different training data were applied: Dinu¿s English¿Italian benchmark data, and English¿Italian translation pairs extracted from the PanLex database. In the second case, only the PanLex database was used. The system performs on English¿Italian languages with the best setting significantly better than the baseline system given by Mikolov, and it provides a comparable performance with more sophisticated systems. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages. ; This research was funded by Spanish MINECO and FEDER grant number TIN2017-85854-C4-2-R. ; Castro-Bleda, MJ.; Iklódi, E.; Recski, G.; Borbély, G. (2019). Towards a Universal Semantic Dictionary. Applied Sciences. 9(19):1-14. https://doi.org/10.3390/app9194060 ; S ; 1 ; 14 ; 9 ; 19 ; Youn, H., Sutton, L., Smith, E., Moore, C., Wilkins, J. F., Maddieson, I., … Bhattacharya, T. (2016). On the universal structure of human lexical semantics. Proceedings of the National Academy of Sciences, 113(7), 1766-1771. doi:10.1073/pnas.1520752113 ; Ruder, S., Vulić, I., & Søgaard, A. (2019). A Survey of Cross-lingual Word Embedding Models. Journal of Artificial Intelligence Research, 65, 569-631. doi:10.1613/jair.1.11640 ; Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135-146. doi:10.1162/tacl_a_00051
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Keyword:
Artificial neural networks; LENGUAJES Y SISTEMAS INFORMATICOS; Multilingual embeddings; Natural language processing; Semantics; Translation; Word embeddings
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URL: https://doi.org/10.3390/app9194060
http://hdl.handle.net/10251/139157
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Contributions to the joint segmentation and classification of sequences (My two cents on decoding and handwriting recognition)
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Neural network language models to select the best translation
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