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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 1771–1780 of 4002 papers

TitleStatusHype
BERTrade: Using Contextual Embeddings to Parse Old French—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
A Distribution-based Model to Learn Bilingual Word Embeddings—0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings—0
Identification of Indigenous Knowledge Concepts through Semantic Networks, Spelling Tools and Word Embeddings—0
BERTMap: A BERT-based Ontology Alignment System—0
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