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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 2481–2490 of 4002 papers

TitleStatusHype
Consistency and Variation in Kernel Neural Ranking Model—0
Predictive Embeddings for Hate Speech Detection on Twitter—0
Learning and Evaluating Sparse Interpretable Sentence Embeddings—0
Predicting the Argumenthood of English Prepositional Phrases—0
FRAGE: Frequency-Agnostic Word RepresentationCode0
Meta-Embedding as Auxiliary Task Regularization—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation—0
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional NetworksCode0
Generalizing Word Embeddings using Bag of SubwordsCode0
Distilled Wasserstein Learning for Word Embedding and Topic Modeling—0
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