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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 1431–1440 of 4002 papers

TitleStatusHype
Evaluating word embeddings with fMRI and eye-tracking—0
Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms—0
Evaluation methods for unsupervised word embeddings—0
Evaluation of acoustic word embeddings—0
Classification Attention for Chinese NER—0
Evaluation of Deep Learning Models for Hostility Detection in Hindi Text—0
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings—0
Evaluation of Domain-specific Word Embeddings using Knowledge Resources—0
Evaluation of Greek Word Embeddings—0
Word Embedding based New Corpus for Low-resourced Language: Sindhi—0
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