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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 431–440 of 4002 papers

TitleStatusHype
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Automatic Transformation of Clinical Narratives into Structured Format—0
A Syllable-based Technique for Word Embeddings of Korean Words—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
A Methodology for Studying Linguistic and Cultural Change in China, 1900-1950—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Analysis of Italian Word Embeddings—0
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations—0
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification—0
Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics—0
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