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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 2321–2330 of 4002 papers

TitleStatusHype
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings—0
Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers—0
SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning—0
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision—0
SWOW-8500: Word Association task for Intrinsic Evaluation of Word Embeddings—0
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction—0
Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives—0
Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling—0
Synonym Discovery with Etymology-based Word Embeddings—0
Syntactic and semantic classification of verb arguments using dependency-based and rich semantic features—0
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