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- Better Word Embeddings by Disentangling Contextual n-Gram Information
Pre-trained word vectors are ubiquitous in Natural Language Processing applications In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings
- arXiv:1904. 05033v1 [cs. CL] 10 Apr 2019 - ResearchGate
Recently, significant improvements in the qual-ity of the word embeddings were obtained by * indicates equal contribution
- Better Word Embeddings by Disentangling Contextual n-Gram Information
Paper Details: Month: JuneYear: 2019Location: Minneapolis, MinnesotaVenue: NAACL |
- Better Word Embeddings by Disentangling Contextual n-Gram Information
Pre-trained word vectors are ubiquitous in Natural Language Processing applications In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings
- GitHub Pages - Matteo Pagliardini
I am a final-year Ph D candidate at the Swiss Federal Institute of Technology in Lausanne (EPFL), in the EDIC doctoral program I am co-supervised by Prof Martin Jaggi and Prof François Fleuret in the laboratory of Machine Learning and Optimization (MLO)
- Better Word Embeddings by Disentangling . . .
We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks
- Better Word Embeddings by Disentangling Contextual n-Gram Information
Abstract Pre-trained word vectors are ubiquitous in Natural Language Processing applications In this paper, we show how training word em-beddings jointly with bigram and even trigram embeddings, results in improved unigram em-beddings
- Better Word Embeddings by Disentangling Contextual n-Gram Information
This paper claims that training word embeddings along with higher n-gram embeddins helps in the removal of the contextual information from the unigrams, resulting in better stand-alone word embedDings, and empirically shows the validity of this hypothesis
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