2021
EMNLP
EMNLP 2021
Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing
Abstract
AbstractIn this paper we investigate the recently proposed multi-input RIM for inspectability. This framework follows an encapsulation paradigm, where external knowledge sources are encoded as largely independent modules, enabling transparency for model inspection.
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Interdisciplinary Bridge
— Artificial Intelligence and Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— encapsulation paradigm
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Reinforcement Learning, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Natural Language Processing > Applications > Information Extraction
Healthcare & Medicine > Clinical > Clinical NLP
Healthcare & Medicine > Research > Bioinformatics
Machine Learning > Core Methods > Probabilistic Modeling
Healthcare & Medicine > Clinical > Medical NLP
Natural Language Processing > Applications > Text Processing