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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 2051–2075 of 4002 papers

TitleStatusHype
Real-Time Keyword Extraction from Conversations—0
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds—0
Recent Developments within BulTreeBank—0
Recognition of Hyponymy and Meronymy Relations in Word Embeddings for Polish—0
Recognition of Implicit Geographic Movement in Text—0
Recognizing Humour using Word Associations and Humour Anchor Extraction—0
Recognizing Plans by Learning Embeddings from Observed Action Distributions—0
Recognizing Salient Entities in Shopping Queries—0
Recognizing Textual Entailment in Twitter Using Word Embeddings—0
Recognizing UMLS Semantic Types with Deep Learning—0
Recommender systems, stigmergy, and the tyranny of popularity—0
Reconstructing Word Embeddings via Scattered k-Sub-Embedding—0
Reconstruction of Word Embeddings from Sub-Word Parameters—0
Recovering Structured Probability Matrices—0
ReDDIT: Regret Detection and Domain Identification from Text—0
Redefining part-of-speech classes with distributional semantic models—0
Reducing Gender Bias in Abusive Language Detection—0
Reducing Lexical Features in Parsing by Word Embeddings—0
Re-embedding words—0
ReferenceNet: a semantic-pragmatic network for capturing reference relations.—0
Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation—0
Refining Word Embeddings for Sentiment Analysis—0
Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal—0
Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency—0
Regular polysemy: from sense vectors to sense patterns—0
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