SeNsER: Learning Cross-Building Sensor Metadata Tagger

Yang Jiao, Jiacheng Li, Jiaman Wu, Dezhi Hong, Rajesh Gupta, Jingbo Shang


Abstract
Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e.g., measurement type and location) about sensors for running smart building applications. Unfortunately, sensor metadata in different buildings often follows distinct naming conventions. Therefore, learning a tagger currently requires extensive annotations on a per building basis. In this work, we propose a novel framework, SeNsER, which learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. It leverages the commonality between different buildings: At the character level, it employs bidirectional neural language models to capture the shared underlying patterns between two buildings and thus regularizes the feature learning process; At the word level, it leverages as features the k-mers existing in the fully annotated building. During inference, we further incorporate the information obtained from sources such as Wikipedia as prior knowledge. As a result, SeNsER shows promising results in extensive experiments on multiple real-world buildings.
Anthology ID:
2020.findings-emnlp.85
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
950–960
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.85
DOI:
10.18653/v1/2020.findings-emnlp.85
Bibkey:
Cite (ACL):
Yang Jiao, Jiacheng Li, Jiaman Wu, Dezhi Hong, Rajesh Gupta, and Jingbo Shang. 2020. SeNsER: Learning Cross-Building Sensor Metadata Tagger. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 950–960, Online. Association for Computational Linguistics.
Cite (Informal):
SeNsER: Learning Cross-Building Sensor Metadata Tagger (Jiao et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.85.pdf
Code
 jiachengli1995/senser