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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 2381–2390 of 4002 papers

TitleStatusHype
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets—0
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval—0
Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing—0
Text Similarity Using Word Embeddings to Classify Misinformation—0
Textual Aggression Detection through Deep Learning—0
Textual Data for Time Series Forecasting—0
TF-IDF Character N-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study—0
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction—0
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale—0
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