2017
ACL
ACL 2017
Discourse Mode Identification in Essays
Abstract
AbstractDiscourse modes play an important role in writing composition and evaluation. This paper presents a study on the manual and automatic identification of narration,exposition, description, argument and emotion expressing sentences in narrative essays. We annotate a corpus to study the characteristics of discourse modes and describe a neural sequence labeling model for identification. Evaluation results show that discourse modes can be identified automatically with an average F1-score of 0.7. We further demonstrate that discourse modes can be used as features that improve automatic essay scoring (AES). The impacts of discourse modes for AES are also discussed.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Trend Setter
— Semantic Analysis
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Keyword Pioneer
— discourse mode identification
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Hot Topic Early Bird
— text classification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
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Topic Pioneer
— Sequence Labeling
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Linguistics > Computational Linguistics
Machine Learning > Core Methods > Sequence Labeling
Natural Language Processing > Applications > Natural Language Understanding
Deep Learning > Learning Types > Sequence Labeling