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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 701–710 of 4002 papers

TitleStatusHype
Query Obfuscation by Semantic Decomposition—0
Accurate Dependency Parsing and Tagging of Latin—0
Dialects Identification of Armenian Language—0
Casteism in India, but Not Racism - a Study of Bias in Word Embeddings of Indian Languages—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese—0
Sentence Selection Strategies for Distilling Word Embeddings from BERT—0
A Hmong Corpus with Elaborate Expression Annotations—0
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches—0
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