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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 771–780 of 4002 papers

TitleStatusHype
Offensive Language Detection with BERT-based models, By Customizing Attention Probabilities—0
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference ResolutionCode0
Using Word Embeddings for Italian Crime News Categorization—0
Human-in-the-Loop Refinement of Word Embeddings—0
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy—0
A Survey On Neural Word Embeddings—0
A Case Study to Reveal if an Area of Interest has a Trend in Ongoing Tweets Using Word and Sentence Embeddings—0
Keyword-centered Collocating Topic Analysis—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases—0
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