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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 1211–1220 of 4002 papers

TitleStatusHype
Topology of Word Embeddings: Singularities Reflect Polysemy—0
Argumentative Topology: Finding Loop(holes) in Logic—0
Debiasing Convolutional Neural Networks via Meta OrthogonalizationCode0
Learning language variations in news corpora through differential embeddingsCode0
Deconstructing word embedding algorithms—0
Exploring the Value of Personalized Word Embeddings—0
Multilingual Irony Detection with Dependency Syntax and Neural ModelsCode0
Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks—0
Positional Artefacts Propagate Through Masked Language Model Embeddings—0
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian LanguagesCode1
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