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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 1621–1630 of 4002 papers

TitleStatusHype
An evaluation of Czech word embeddings—0
Fully Delexicalized Contexts for Syntax-Based Word Embeddings—0
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts—0
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter—0
Complementary Strategies for Low Resourced Morphological Modeling—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion approaches for emotion recognition from speech using acoustic and text-based features—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
Complex Ontology Matching with Large Language Model Embeddings—0
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification—0
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