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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 1011–1020 of 4002 papers

TitleStatusHype
RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction—0
Modeling Text using the Continuous Space Topic Model with Pre-Trained Word Embeddings—0
Measure and Evaluation of Semantic Divergence across Two Languages—0
SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection—0
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization—0
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings—0
Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers—0
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