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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 3551–3560 of 4002 papers

TitleStatusHype
Subsumption Preservation as a Comparative Measure for Evaluating Sense-Directed Embeddings—0
Evaluation of acoustic word embeddings—0
Evaluating word embeddings with fMRI and eye-tracking—0
Evaluating Word Embeddings Using a Representative Suite of Practical Tasks—0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation—0
Spanish NER with Word Representations and Conditional Random Fields—0
SimpleNets: Quality Estimation with Resource-Light Neural Networks—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
The Kyoto University Cross-Lingual Pronoun Translation System—0
Sentence Embedding Evaluation Using Pyramid Annotation—0
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