@inproceedings{jiang-riloff-2018-learning,
title = "Learning Prototypical Goal Activities for Locations",
author = "Jiang, Tianyu and
Riloff, Ellen",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1120",
doi = "10.18653/v1/P18-1120",
pages = "1297--1307",
abstract = "People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators.",
}
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%0 Conference Proceedings
%T Learning Prototypical Goal Activities for Locations
%A Jiang, Tianyu
%A Riloff, Ellen
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jiang-riloff-2018-learning
%X People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators.
%R 10.18653/v1/P18-1120
%U https://aclanthology.org/P18-1120
%U https://doi.org/10.18653/v1/P18-1120
%P 1297-1307
Markdown (Informal)
[Learning Prototypical Goal Activities for Locations](https://aclanthology.org/P18-1120) (Jiang & Riloff, ACL 2018)
ACL
- Tianyu Jiang and Ellen Riloff. 2018. Learning Prototypical Goal Activities for Locations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1297–1307, Melbourne, Australia. Association for Computational Linguistics.