This archive contains a collection of computational models called word embeddings. These are vectors that contain numerical representations of words. These have been trained on real language sentences collected from the English Wikipedia. As such, they contain contextual (thematic) knowledge about words (rather than taxonomic)
What is a word embedding? Suppose you have a dictionary of words. The i th word in the dictionary is...
We propose a new model for learning bilingual word representations from non-parallel document-aligne...
Real-valued word embeddings have transformed natural language processing (NLP) applications, recogni...
This archive contains a collection of computational models called word embeddings. These are vectors...
This archive contains a collection of computational models called word embeddings. These are vectors...
Obtaining Better Static Word Embeddings Using Contextual Embedding Models This repository contains ...
Research on word representation has always been an important area of interest in the antiquity of Na...
International audienceRecent studies in the biomedical domain suggest that learning statistical word...
Word embeddings serve as an useful resource for many downstream natural language processing tasks. T...
Static word embeddings that represent words by a single vector cannot capture the variability of wor...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, ...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Word representation or word embedding is an important step in understanding languages. It maps simil...
The present paper intends to draw the conception of language implied in the technique of word embedd...
What is a word embedding? Suppose you have a dictionary of words. The i th word in the dictionary is...
We propose a new model for learning bilingual word representations from non-parallel document-aligne...
Real-valued word embeddings have transformed natural language processing (NLP) applications, recogni...
This archive contains a collection of computational models called word embeddings. These are vectors...
This archive contains a collection of computational models called word embeddings. These are vectors...
Obtaining Better Static Word Embeddings Using Contextual Embedding Models This repository contains ...
Research on word representation has always been an important area of interest in the antiquity of Na...
International audienceRecent studies in the biomedical domain suggest that learning statistical word...
Word embeddings serve as an useful resource for many downstream natural language processing tasks. T...
Static word embeddings that represent words by a single vector cannot capture the variability of wor...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, ...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Word representation or word embedding is an important step in understanding languages. It maps simil...
The present paper intends to draw the conception of language implied in the technique of word embedd...
What is a word embedding? Suppose you have a dictionary of words. The i th word in the dictionary is...
We propose a new model for learning bilingual word representations from non-parallel document-aligne...
Real-valued word embeddings have transformed natural language processing (NLP) applications, recogni...
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