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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 3581–3590 of 4002 papers

TitleStatusHype
Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications—0
DeepTC -- An Extension of DKPro Text Classification for Fostering Reproducibility of Deep Learning Experiments—0
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases—0
"Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics—0
Delexicalized Word Embeddings for Cross-lingual Dependency Parsing—0
Delta Embedding Learning—0
De-Mixing Sentiment from Code-Mixed Text—0
Demographic Word Embeddings for Racism Detection on Twitter—0
Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery—0
Denoising Word Embeddings by Averaging in a Shared Space—0
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