@inproceedings{niraula-etal-2018-novel,
    title = "A Novel Approach to Part Name Discovery in Noisy Text",
    author = "Niraula, Nobal Bikram  and
      Whyatt, Daniel  and
      Kao, Anne",
    editor = "Bangalore, Srinivas  and
      Chu-Carroll, Jennifer  and
      Li, Yunyao",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-3021/",
    doi = "10.18653/v1/N18-3021",
    pages = "170--176",
    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."
}