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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 1021–1030 of 4002 papers

TitleStatusHype
Deeper Attention to Abusive User Content Moderation—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
Binary Encoded Word Mover’s Distance—0
A Helping Hand: Transfer Learning for Deep Sentiment Analysis—0
Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction—0
Query Obfuscation Semantic Decomposition—0
Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling—0
Des pseudo-sens pour am\'eliorer l'extraction de synonymes \`a partir de plongements lexicaux (Pseudo-senses for improving the extraction of synonyms from word embeddings)—0
Detecting and Mitigating Indirect Stereotypes in Word Embeddings—0
Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis—0
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