{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sensei-self-supervised-sensor-name","title":"Sensei: Self-Supervised Sensor Name Segmentation","arxiv_id":"2101.00130","date":"2021-01-01","proceeding":"Findings (ACL) 2021 8","authors":["Jiaman Wu","Dezhi Hong","Rajesh Gupta","Jingbo Shang"],"abstract":"A sensor name, typically an alphanumeric string, encodes the key context (e.g., function and location) of a sensor needed for deploying smart building applications. Sensor names, however, are curated in a building vendor-specific manner using different structures and vocabularies that are often esoteric. They thus require tremendous manual effort to annotate on a per-building basis; even to just segment these sensor names into meaningful chunks. In this paper, we propose a fully automated self-supervised framework, Sensei, which can learn to segment sensor names without any human annotation. Specifically, we employ a neural language model to capture the underlying sensor naming structure and then induce self-supervision based on information from the language model to build the segmentation model. Extensive experiments on five real-world buildings comprising thousands of sensors demonstrate the superiority of Sensei over baseline methods.","url_abs":"https://arxiv.org/abs/2101.00130v1","url_pdf":"https://arxiv.org/pdf/2101.00130v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sensei-self-supervised-sensor-name","repo_url":"https://github.com/work4cs/sensei","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.00130","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}