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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 1861–1870 of 4002 papers

TitleStatusHype
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces—0
Clinical Named Entity Recognition using Contextualized Token Representations—0
ArGoT: A Glossary of Terms extracted from the arXiv—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter—0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—0
Example-based Acquisition of Fine-grained Collocation Resources—0
Incorporating Context into Language Encoding Models for fMRI—0
Clinical Event Detection with Hybrid Neural Architecture—0
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework—0
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