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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 1211–1220 of 4002 papers

TitleStatusHype
Domain adaptation for part-of-speech tagging of noisy user-generated text—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval—0
Comparative analysis of word embeddings in assessing semantic similarity of complex sentences—0
Developing Conversational Data and Detection of Conversational Humor in Telugu—0
Do Not Harm Protected Groups in Debiasing Language Representation Models—0
Do not neglect related languages: The case of low-resource Occitan cross-lingual word embeddings—0
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Beyond Context: A New Perspective for Word Embeddings—0
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