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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 1381–1390 of 4002 papers

TitleStatusHype
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations—0
Contextualized context2vec—0
Contextualization and Generalization in Entity and Relation Extraction—0
Analysis of Italian Word Embeddings—0
Contextual Embeddings: When Are They Worth It?—0
Contextual Document Embeddings—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Contextual Aware Joint Probability Model Towards Question Answering System—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
A Syllable-based Technique for Word Embeddings of Korean Words—0
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