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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 3941–3950 of 4002 papers

TitleStatusHype
Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training—0
Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor—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
Faster Training of Word Embeddings—0
Fast query-by-example speech search using separable model—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
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