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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 1271–1280 of 4002 papers

TitleStatusHype
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition—0
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning—0
Cross-lingual Transfer of Sentiment Classifiers—0
Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Cross-Lingual Transfer Learning for Hate Speech Detection—0
Automated Discovery of Mathematical Definitions in Text—0
Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention—0
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