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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 3151–3160 of 4002 papers

TitleStatusHype
A Survey Of Cross-lingual Word Embedding Models—0
A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches—0
A Survey On Neural Word Embeddings—0
A Survey on Word Meta-Embedding Learning—0
A Syllable-based Technique for Word Embeddings of Korean Words—0
Asymmetric Proxy Loss for Multi-View Acoustic 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
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
A Topical Approach to Capturing Customer Insight In Social Media—0
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