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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 3951–3960 of 4002 papers

TitleStatusHype
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations PredictionCode0
Spoken Word2Vec: Learning Skipgram Embeddings from SpeechCode0
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information PreservingCode0
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Identification of Adjective-Noun Neologisms using Pretrained Language ModelsCode0
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword RepresentationsCode0
Analysis of Railway Accidents' Narratives Using Deep LearningCode0
A Review of Different Word Embeddings for Sentiment Classification using Deep LearningCode0
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