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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 1391–1400 of 4002 papers

TitleStatusHype
Autoencoding Improves Pre-trained Word Embeddings—0
Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge—0
Word Embeddings for Chemical Patent Natural Language Processing—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method—0
Topic Modeling with Contextualized Word Representation Clusters—0
Generating Adequate Distractors for Multiple-Choice Questions—0
Comparative analysis of word embeddings in assessing semantic similarity of complex sentences—0
Learning Graph-Based Priors for Generalized Zero-Shot Learning—0
On the Effects of Using word2vec Representations in Neural Networks for Dialogue Act Recognition—0
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