2017 EMNLP EMNLP 2017

Classification of telicity using cross-linguistic annotation projection

Abstract

AbstractThis paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint (“she walked home”), while an atelic event does not (“she walked around”). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb’s meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary and Machine Learning and Natural Language Processing
📈 Trend Setter — Semantic Analysis
🧭 Keyword Pioneer — telicity 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