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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 1521–1530 of 4002 papers

TitleStatusHype
Extending and Improving Wordnet via Unsupervised Word Embeddings—0
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications—0
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)—0
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning—0
Extracting domain-specific terms using contextual word embeddings—0
Extracting Possessions from Social Media: Images Complement Language—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
Extracting Tags from Large Raw Texts Using End-to-End Memory Networks—0
Extracting Temporal and Causal Relations between Events—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
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