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2019 ACL ACL 2019

ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets

Abstract

AbstractThis paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🧭 Keyword Pioneer — aspect classification
🐝 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

Skatje Myers , Martha Palmer

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks Deep Learning > Techniques > Model Architecture

Keywords

sequence labeling conditional random field bidirectional lstm neural network verb tense aspect classification
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