2017
EACL
EACL 2017
Nonsymbolic Text Representation
Abstract
AbstractWe introduce the first generic text representation model that is completely nonsymbolic, i.e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text. This applies to training the parameters of the model on a training corpus as well as to applying it when computing the representation of a new text. We show that our model performs better than prior work on an information extraction and a text denoising task.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Trend Setter
— Large Language Models
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Keyword Pioneer
— nonsymbolic representation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Models > Generative Models
Natural Language Processing > Resources & Methods > Text Representation
Machine Learning > Learning Types > Representation Learning
Deep Learning > Models > Large Language Models
Deep Learning > Learning Types > Self-Supervised Learning