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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 3981–3990 of 4002 papers

TitleStatusHype
From cart to truck: meaning shift through words in English in the last two centuries—0
From communities to interpretable network and word embedding: an unified approach—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
From meaning to perception -- exploring the space between word and odor perception embeddings—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
From Raw Text to Universal Dependencies - Look, No Tags!—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers—0
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