SeNsER: Learning Cross-Building Sensor Metadata Tagger
SeNsER: Learning Cross-Building Sensor Metadata Tagger
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DOI：
10.18653/v1/2020.findings-emnlp.85
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Yang Jiao;Jiacheng Li;Jiaman Wu;Dezhi Hong;Rajesh K. Gupta;Jingbo Shang
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文献类型：
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作者：
Yang Jiao;Jiacheng Li;Jiaman Wu;Dezhi Hong;Rajesh K. Gupta;Jingbo Shang
类似于命名实体识别任务的传感器元数据标记提供关键上下文信息（例如，测量类型和位置）关于用于运行智能建筑应用的传感器。不幸的是，不同建筑物中的传感器元数据通常遵循不同的命名约定。因此，学习标记器目前需要在每个建筑物的基础上进行广泛的注释。在这项工作中，我们提出了一个新颖的框架SeNsER，它根据新建筑的原始元数据和一些现有的完全注释的建筑来学习新建筑的传感器元数据标记器。它利用了不同建筑物之间的共性：在字符层面，它采用双向神经语言模型来捕获两个建筑物之间共享的底层模式，从而规范化特征学习过程;在单词层面，它利用了完全注释的建筑物中存在的k-mers作为特征。在推理过程中，我们进一步将从维基百科等来源获得的信息作为先验知识。因此，SeNsER在多个真实世界建筑物的广泛实验中显示出有希望的结果。
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.