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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 2211–2220 of 4002 papers

TitleStatusHype
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings—0
Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches—0
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages—0
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings—0
Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks—0
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model—0
Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection—0
SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)—0
Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach—0
Sex Trafficking Detection with Ordinal Regression Neural Networks—0
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