A Novel Approach to Part Name Discovery in Noisy Text

Nobal Bikram Niraula, Daniel Whyatt, Anne Kao


Abstract
As a specialized example of information extraction, part name extraction is an area that presents unique challenges. Part names are typically multi-word terms longer than two words. There is little consistency in how terms are described in noisy free text, with variations spawned by typos, ad hoc abbreviations, acronyms, and incomplete names. This makes search and analyses of parts in these data extremely challenging. In this paper, we present our algorithm, PANDA (Part Name Discovery Analytics), based on a unique method that exploits statistical, linguistic and machine learning techniques to discover part names in noisy text such as that in manufacturing quality documentation, supply chain management records, service communication logs, and maintenance reports. Experiments show that PANDA is scalable and outperforms existing techniques significantly.
Anthology ID:
N18-3021
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
Month:
June
Year:
2018
Address:
New Orleans - Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
170–176
Language:
URL:
https://www.aclweb.org/anthology/N18-3021
DOI:
10.18653/v1/N18-3021
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-3021.pdf
Video:
 http://vimeo.com/277669493