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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 2351–2360 of 4002 papers

TitleStatusHype
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors—0
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection—0
Tales and Tropes: Gender Roles from Word Embeddings in a Century of Children’s Books—0
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding—0
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
Target word activity detector: An approach to obtain ASR word boundaries without lexicon—0
Task-adaptive Pre-training of Language Models with Word Embedding Regularization—0
Task-oriented Domain-specific Meta-Embedding for Text Classification—0
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification—0
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