Abstract
Processing maintenance logbook records is an important step in the development of predictive maintenance systems. Logbooks often include free text fields with domain specific terms, abbreviations, and non-standard spelling posing challenges to off-the-shelf NLP pipelines trained on standard contemporary corpora. Despite the importance of this data type, processing predictive maintenance data is still an under-explored topic in NLP. With the goal of providing more datasets and resources to the community, in this paper we present a number of new resources available in MaintNet, a collaborative open-source library and data repository of predictive maintenance language datasets. We describe novel annotated datasets from multiple domains such as aviation, automotive, and facility maintenance domains and new tools for segmentation, spell checking, POS tagging, clustering, and classification.- Anthology ID:
- 2020.aacl-demo.5
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Derek Wong, Douwe Kiela
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–32
- Language:
- URL:
- https://aclanthology.org/2020.aacl-demo.5
- DOI:
- Cite (ACL):
- Farhad Akhbardeh, Travis Desell, and Marcos Zampieri. 2020. NLP Tools for Predictive Maintenance Records in MaintNet. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations, pages 26–32, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- NLP Tools for Predictive Maintenance Records in MaintNet (Akhbardeh et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.aacl-demo.5.pdf