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Title: SeNsER: Learning Cross-Building Sensor Metadata Tagger
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 dis- tinct 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.  more » « less
Award ID(s):
2040727
NSF-PAR ID:
10250428
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Findings of the Association for Computational Linguistics: EMNLP 2020
Page Range / eLocation ID:
950 to 960
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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