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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 3491–3500 of 4002 papers

TitleStatusHype
COVID-19 and Arabic Twitter: How can Arab World Governments and Public Health Organizations Learn from Social Media?—0
Creating Causal Embeddings for Question Answering with Minimal Supervision—0
Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain—0
Cross-Document Narrative Alignment of Environmental News: A Position Paper on the Challenge of Using Event Chains to Proxy Narrative Features—0
Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models—0
Cross-language Learning with Adversarial Neural Networks—0
Cross-Language Question Re-Ranking—0
Cross-lingual alignments of ELMo contextual embeddings—0
Cross-Lingual Classification of Topics in Political Texts—0
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind—0
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