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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 1381–1390 of 4002 papers

TitleStatusHype
Estimating User Communication Styles for Spoken Dialogue Systems—0
Estimating word co-occurrence probabilities from pretrained static embeddings using a log-bilinear model—0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension—0
\'Etiquetage en parties du discours de langues peu dot\'ees par sp\'ecialisation des plongements lexicaux (POS tagging for low-resource languages by adapting word embeddings )—0
Arabic aspect sentiment polarity classification using BERT—0
Etude de la reproductibilit\'e des word embeddings : rep\'erage des zones stables et instables dans le lexique (Reproducibility of word embeddings : identifying stable and unstable zones in the semantic space)—0
Detecting Sarcasm Using Different Forms Of Incongruity—0
EusDisParser: improving an under-resourced discourse parser with cross-lingual data—0
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages—0
Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work—0
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