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Learning character-wise text representations with Elman nets
Grzegorz Chrupala
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Dive into the research topics of 'Learning character-wise text representations with Elman nets'. Together they form a unique fingerprint.
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Keyphrases
Simple Recurrent Network
100%
Text Representation
100%
Elman
100%
Sentence Segmentation
66%
Word Segmentation
66%
Character-level
66%
Recurrent Neural Network
66%
N-gram
66%
Language Model
66%
Text Analysis
66%
Deep Learning
33%
Dutch
33%
Unseen
33%
Audio
33%
Token
33%
Small Units
33%
Vector Space
33%
Feedforward Neural Network
33%
Sequential Structure
33%
Natural Language Processing
33%
Error Reduction
33%
Cognitive Science
33%
Sequence Labeling
33%
Elephant
33%
Network Applications
33%
Neural Embeddings
33%
Canonical Form
33%
Tweet Normalization
33%
Edit Script
33%
Tweets
33%
Text Embedding
33%
Non-canonical
33%
Machine Learning
33%
Computational Linguistics
33%
Abstract Representation
33%
Hidden Layer
33%
Text Segmentation
33%
Characteristic Sequence
33%
Temporal Structure
33%
Word Embedding
33%
Informative Features
33%
Natural Language Text
33%
User-generated Content
33%
Learning Research
33%
Reliable Performance
33%
Relative Error
33%
Hidden Unit
33%
Parallel Development
33%
Performance Boost
33%
Representation of History
33%
One-hot Vector
33%
Neural Language Models
33%
N-gram Model
33%
Semantic Compositionality
33%
Supervised Learning Model
33%
Task Learning
33%
Matrix-vector
33%
Normalised Form
33%
Orthographic Word
33%
Compressed Representation
33%
Recursive Matrix
33%
Computer Science
Text Representation
100%
Language Modeling
100%
Recurrent Network
66%
Recurrent Neural Network
66%
Natural Language Processing
33%
Canonical Form
33%
Computational Linguistics
33%
Abstract Representation
33%
User-Generated Content
33%
Supervised Learning
33%
Relative Error
33%
n-gram language model
33%
Level Representation
33%
Normalized Form
33%
Text Segment
33%
Deep Learning Method
33%
Machine Learning
33%
Learning System
33%
Text Segmentation
33%
Word Embedding
33%
Feedforward Neural Network
33%
Mathematics
Neural Network
100%
Matrix (Mathematics)
33%
Learning Task
33%
Relative Error
33%
Generated Content
33%
Hidden Unit
33%
Canonical Form
33%
Input Feature
33%
Deep Learning Method
33%
Word Embedding
33%
Vector Space
33%
Sequence Labeling
33%
Psychology
Neural Network
100%
Cognitive Science
33%
Learning Model
33%