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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 861–870 of 4002 papers

TitleStatusHype
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification—0
A Survey On Neural Word Embeddings—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Context-Sensitive Malicious Spelling Error Correction—0
Context Sensitive Neural Lemmatization with Lematus—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
Contextual Aware Joint Probability Model Towards Question Answering System—0
Contextual Document Embeddings—0
A novel methodology on distributed representations of proteins using their interacting ligands—0
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