Dezhi Hong


2021

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Sensei: Self-Supervised Sensor Name Segmentation
Jiaman Wu | Dezhi Hong | Rajesh Gupta | Jingbo Shang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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SeNsER: Learning Cross-Building Sensor Metadata Tagger
Yang Jiao | Jiacheng Li | Jiaman Wu | Dezhi Hong | Rajesh Gupta | Jingbo Shang
Findings of the Association for Computational Linguistics: EMNLP 2020

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.