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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 2241–2250 of 4002 papers

TitleStatusHype
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Attentive Mimicking: Better Word Embeddings by Attending to Informative ContextsCode0
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation—0
Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings—0
Multimodal Machine Translation with Embedding PredictionCode0
SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings EvaluationCode0
Integrating Semantic Knowledge to Tackle Zero-shot Text ClassificationCode0
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition—0
Learning semantic sentence representations from visually grounded language without lexical knowledgeCode0
Deep Learning and Word Embeddings for Tweet Classification for Crisis Response—0
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