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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 931–940 of 4002 papers

TitleStatusHype
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Large Scale Substitution-based Word Sense Induction—0
BI-RADS BERT & Using Section Segmentation to Understand Radiology ReportsCode0
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning—0
Regionalized models for Spanish language variations based on Twitter—0
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
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