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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 321–330 of 4002 papers

TitleStatusHype
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings—0
Ask the GRU: Multi-Task Learning for Deep Text Recommendations—0
A comparative analysis of embedding models for patent similarity—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
A Simple Disaster-Related Knowledge Base for Intelligent Agents—0
A Deep Neural Framework for Contextual Affect Detection—0
Analogies Explained: Towards Understanding Word Embeddings—0
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
A Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars—0
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