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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 1501–1510 of 4002 papers

TitleStatusHype
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Estimating Mutual Information Between Dense Word Embeddings—0
Character aware models with similarity learning for metaphor detection—0
Whole-Word Segmental Speech Recognition with Acoustic Word EmbeddingsCode0
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification—0
Neural Metaphor Detection with a Residual biLSTM-CRF Model—0
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent—0
Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings—0
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
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