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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 2401–2410 of 4002 papers

TitleStatusHype
Unsupervised Parallel Sentence Extraction from Comparable Corpora—0
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings—0
Complementary Strategies for Low Resourced Morphological Modeling—0
IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification—0
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets—0
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction—0
Sounds Wilde. Phonetically Extended Embeddings for Author-Stylized Poetry Generation—0
An Unsupervised System for Parallel Corpus Filtering—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task—0
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