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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 1911–1920 of 4002 papers

TitleStatusHype
Instantiation—0
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition—0
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction—0
Event Ordering with a Generalized Model for Sieve Prediction Ranking—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech—0
Integrating Reviews into Personalized Ranking for Cold Start Recommendation—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings—0
Event Detection Using Frame-Semantic Parser—0
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