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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 1881–1890 of 4002 papers

TitleStatusHype
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus—0
Clinical Abbreviation Disambiguation Using Neural Word Embeddings—0
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings—0
Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation—0
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources—0
Inducing Relational Knowledge from BERT—0
Clickbait detection using word embeddings—0
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?—0
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