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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 1541–1550 of 4002 papers

TitleStatusHype
Combining Discourse Markers and Cross-lingual Embeddings for Synonym--Antonym Classification—0
Detecting Figurative Word Occurrences Using Recurrent Neural Networks—0
Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods—0
Fashioning Data - A Social Media Perspective on Fast Fashion Brands—0
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search—0
Combining neural and knowledge-based approaches to Named Entity Recognition in Polish—0
Faster Training of Word Embeddings—0
Detecting Fake News with Capsule Neural Networks—0
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