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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 2141–2150 of 4002 papers

TitleStatusHype
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing—0
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification—0
Beyond Context: A New Perspective for Word Embeddings—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings—0
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?Code0
THU\_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers—0
sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech Detection—0
Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data—0
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble LearningCode0
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