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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 3721–3730 of 4002 papers

TitleStatusHype
Word Embedding Evaluation and Combination—0
Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation—0
Neural Scoring Function for MST Parser—0
A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings—0
Distance Metric Learning for Aspect Phrase Grouping—0
Extracting Temporal and Causal Relations between Events—0
Syntactic and semantic classification of verb arguments using dependency-based and rich semantic features—0
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints—0
From Incremental Meaning to Semantic Unit (phrase by phrase)Code0
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation—0
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