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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 3511–3520 of 4002 papers

TitleStatusHype
Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data—0
Cross-Lingual Transfer Learning for Hate Speech Detection—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Cross-lingual Transfer of Sentiment Classifiers—0
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning—0
Cross-Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon—0
Cross-lingual Word Embeddings beyond Zero-shot Machine Translation—0
Cross-Lingual Word Embeddings for Morphologically Rich Languages—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Cross-lingual Word Embeddings in Hyperbolic Space—0
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