@inproceedings{jiao-etal-2020-senser,
title = "{S}e{N}s{ER}: Learning Cross-Building Sensor Metadata Tagger",
author = "Jiao, Yang and
Li, Jiacheng and
Wu, Jiaman and
Hong, Dezhi and
Gupta, Rajesh and
Shang, Jingbo",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.85/",
doi = "10.18653/v1/2020.findings-emnlp.85",
pages = "950--960",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T SeNsER: Learning Cross-Building Sensor Metadata Tagger
%A Jiao, Yang
%A Li, Jiacheng
%A Wu, Jiaman
%A Hong, Dezhi
%A Gupta, Rajesh
%A Shang, Jingbo
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jiao-etal-2020-senser
%X 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.
%R 10.18653/v1/2020.findings-emnlp.85
%U https://aclanthology.org/2020.findings-emnlp.85/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.85
%P 950-960
Markdown (Informal)
[SeNsER: Learning Cross-Building Sensor Metadata Tagger](https://aclanthology.org/2020.findings-emnlp.85/) (Jiao et al., Findings 2020)
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.