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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 1091–1100 of 4002 papers

TitleStatusHype
A Computational Approach to Measuring the Semantic Divergence of Cognates—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Disambiguated skip-gram model—0
Bidirectional Long Short-Term Memory Networks for Relation Classification—0
An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods—0
A General Method for Event Detection on Social Media—0
Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings—0
Bias in word embeddings—0
Gender bias in (non)-contextual clinical word embeddings for stereotypical medical categories—0
Angular-Based Word Meta-Embedding Learning—0
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