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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 1401–1410 of 4002 papers

TitleStatusHype
Evaluating Metrics for Bias in Word Embeddings—0
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms—0
Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches—0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation—0
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks—0
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View—0
A Framework for Decoding Event-Related Potentials from Text—0
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning—0
Chinese Hypernym-Hyponym Extraction from User Generated Categories—0
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings—0
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