Open AccessW&G-Bert: A Concept for a Pre-Trained Automotive Warranty and Goodwill Language Representation Model for Warranty and Goodwill Text Mining
Author(s) -
Lukas Jonathan Weber,
Alice Kirchheim,
A Zimmermann
Publication year - 2022
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2022.120304
Subject(s) - warranty , goodwill , computer science , natural language processing , artificial intelligence , automotive industry , domain (mathematical analysis) , information retrieval , engineering , mathematics , mathematical analysis , political science , law , aerospace engineering , finance , economics
The request for precise text mining applications to extract information of company based automotive warranty and goodwill (W&G) data is steadily increasing. The progress of the analytical competence of text mining methods for information extraction is among others based on the developments and insights of deep learning techniques applied in natural language processing (NLP). Directly applying NLP based architectures to automotive W&G text mining would wage to a significant performance loss due to different word distributions of general domain and W&G specific corpora. Therefore, labelled W&G training datasets are necessary to transform a general-domain language model in a specific-domain one to increase the performance in W&G text mining tasks.