2019/03/07 by Yiming Xu, Xu, Yiming, Dnyanesh Rajpathak +5 · 1 citation
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Spam and Phishing Detection #Text and Document Classification Technologies #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.1903.04360
openalex publication_date 2019/03/07 · openalex created_date 2019/03/22 · openalex updated_date 2026/07/28
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.