Modality: logic, semantics, annotation and machine learning

Annie Zaenen


Abstract
Up to rather recently Natural Language Processing has not given much attention to modality. As long as the main task was to determined what a text was about (Information Retrieval) or who the participants in an eventuality were (Information Extraction), this neglect was understandable. With the focus moving to questions of natural language understanding and inferencing as well as to sentiment and opinion analysis, it becomes necessary to distinguish between actual and envisioned eventualities and to draw conclusions about the attitude of the writer or speaker towards the eventualities referred to. This means, i.a., to be able to distinguish ‘John went to Paris’ and ‘John wanted to go to Paris’. To do this one has to calculate the effect of different linguistic operators on the eventuality predication.
Anthology ID:
2016.lilt-14.1
Volume:
Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning
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Sept
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2016
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LILT
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CSLI Publications
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https://aclanthology.org/2016.lilt-14.1
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Cite (ACL):
Annie Zaenen. 2016. Modality: logic, semantics, annotation and machine learning. In Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning. CSLI Publications.
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Modality: logic, semantics, annotation and machine learning (Zaenen, LILT 2016)
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https://aclanthology.org/2016.lilt-14.1.pdf