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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 1241–1250 of 4002 papers

TitleStatusHype
The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish—0
Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words—0
Embedding Structured Dictionary Entries—0
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention—0
Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment—0
Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation—0
Robust Backed-off Estimation of Out-of-Vocabulary Embeddings—0
Analysing Word Representation from the Input and Output Embeddings in Neural Network Language ModelsCode0
Word associations and the distance properties of context-aware word embeddings—0
Rethinking Topic Modelling: From Document-Space to Term-Space—0
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