HARE: a Flexible Highlighting Annotator for Ranking and Exploration

Denis Newman-Griffis, Eric Fosler-Lussier


Abstract
Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE.
Anthology ID:
D19-3015
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
85–90
Language:
URL:
https://aclanthology.org/D19-3015
DOI:
10.18653/v1/D19-3015
Bibkey:
Cite (ACL):
Denis Newman-Griffis and Eric Fosler-Lussier. 2019. HARE: a Flexible Highlighting Annotator for Ranking and Exploration. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 85–90, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
HARE: a Flexible Highlighting Annotator for Ranking and Exploration (Newman-Griffis & Fosler-Lussier, EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-3015.pdf
Code
 OSU-slatelab/HARE