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2017 SEMEVAL SemEval 2017

SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation

🐝 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

Gábor Berend

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Representation Learning

Keywords

sequence labeling sparse coding word embedding keyphrase extraction
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