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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 871–880 of 4002 papers

TitleStatusHype
A novel methodology on distributed representations of proteins using their interacting ligands—0
Contextualized context2vec—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing—0
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness—0
Contextualized moral inference—0
Contextualized Spoken Word Representations from Convolutional Autoencoders—0
Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge—0
A Novel Method of Extracting Topological Features from Word Embeddings—0
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