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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 2091–2100 of 4002 papers

TitleStatusHype
Residual Stacking of RNNs for Neural Machine Translation—0
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation—0
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data—0
Rethinking Topic Modelling: From Document-Space to Term-Space—0
Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings—0
Retrofitting Contextualized Word Embeddings with Paraphrases—0
Retrofitting of Pre-trained Emotion Words with VAD-dimensions and the Plutchik Emotions—0
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities—0
RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data—0
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN—0
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