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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 2421–2430 of 4002 papers

TitleStatusHype
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings—0
UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network—0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets—0
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings—0
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification—0
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content—0
Neural Machine Translation of Logographic Language Using Sub-character Level Information—0
Tree-Stack LSTM in Transition Based Dependency ParsingCode0
UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task—0
Vectorial Semantic Spaces Do Not Encode Human Judgments of Intervention Similarity—0
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