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Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both ...
Yang Liu 0005 +3 more
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Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both ...
Yang Liu 0005 +3 more
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Word Embedding Evaluation and Combination
Proceedings of the Language Resources and Evaluation Conference, 2016no ...
Ghannay, Sahar +3 more
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Abstract This chapter deals with the mathematical representation of words through vectors or embeddings which are the basis of modern language models. It starts by discussing the limits of the one-hot representation and continues with a section that presents traditional approaches based on the factorization of the word co-occurrence ...
Christophe Gaillac, Jérémy L'Hour
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Christophe Gaillac, Jérémy L'Hour
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Hybrid embeddings for transition-based dependency parsing of free word order languages
Information Processing and Management, 2023Khalid Saleem, Fatima Tuz Zuhra
exaly
Examining the effect of whitening on static and contextualized word embeddings
Information Processing and Management, 2023Shota Sasaki, Jun Suzuki
exaly
Word Embeddings are Word Story Embeddings (and That's Fine)
2022Katrin Erk, Gabriella Chronis
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Word-class embeddings for multiclass text classification
Data Mining and Knowledge Discovery, 2021Alejandro Moreo Fernández +2 more
exaly