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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 891–900 of 4002 papers

TitleStatusHype
Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation—0
Cross-lingual alignments of ELMo contextual embeddings—0
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
Hate speech detection using static BERT embeddings—0
SAT Based Analogy Evaluation Framework for Persian Word Embeddings—0
A Source-Criticism Debiasing Method for GloVe EmbeddingsCode1
Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource languageCode0
Clinical Named Entity Recognition using Contextualized Token Representations—0
Mixtures of Deep Neural Experts for Automated Speech Scoring—0
Membership Inference on Word Embedding and Beyond—0
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