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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 1371–1380 of 4002 papers

TitleStatusHype
Detecting Semantically Equivalent Questions in Online User Forums—0
Entropy-Based Subword Mining with an Application to Word Embeddings—0
Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area—0
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques—0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension—0
Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models—0
Character aware models with similarity learning for metaphor detection—0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
Estimating Mutual Information Between Dense Word Embeddings—0
Detecting Sarcasm Using Different Forms Of Incongruity—0
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