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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 1261–1270 of 4002 papers

TitleStatusHype
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction—0
Effect of Text Color on Word Embeddings—0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding—0
Effects of Creativity and Cluster Tightness on Short Text Clustering Performance—0
Effects of Word Embeddings on Neural Network-based Pitch Accent Detection—0
``A Passage to India'': Pre-trained Word Embeddings for Indian Languages—0
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering—0
A Cross-lingual Natural Language Processing Framework for Infodemic Management—0
Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks—0
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