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Multi-label, Multi-class Classification Using Polylingual Embeddings
Abstract : We propose a Polylingual text Embedding (PE) strategy, that learns a language independent representation of texts using Neu-ral Networks. We study the effects of bilingual representation learning for text classification and we empirically show that the learned representations achieve better classification performance compared to traditional bag-of-words and other monolingual distributed representations. The performance gains are more significant in the interesting case where only few labeled examples are available for training the classifiers.
Cited literature [12 references]
https://hal.archives-ouvertes.fr/hal-01299850
Contributor : Georgios Balikas Connect in order to contact the contributor
Submitted on : Wednesday, April 20, 2016 - 6:37:53 PM
Last modification on : Wednesday, November 3, 2021 - 6:46:36 AM
Long-term archiving on: : Monday, November 14, 2016 - 10:25:02 PM
Contributor : Georgios Balikas Connect in order to contact the contributor
Submitted on : Wednesday, April 20, 2016 - 6:37:53 PM
Last modification on : Wednesday, November 3, 2021 - 6:46:36 AM
Long-term archiving on: : Monday, November 14, 2016 - 10:25:02 PM
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