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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 3801–3810 of 4002 papers

TitleStatusHype
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification—0
Entity-Centric Contextual Affective Analysis—0
Entity Linking for Queries by Searching Wikipedia Sentences—0
Entropy-Based Subword Mining with an Application to Word Embeddings—0
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques—0
Equipping Educational Applications with Domain Knowledge—0
Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models—0
Estimating Mutual Information Between Dense Word Embeddings—0
Estimating Text Similarity based on Semantic Concept Embeddings—0
Estimating User Communication Styles for Spoken Dialogue Systems—0
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