The recent explosion of advancements in natural language processing (NLP) are encouraging in the industrial sector for leveraging the volumes of unstructured, technical data that currently sit unused. However, results from direct application of many NLP pipelines to technical text often fail to address the business needs of industrial companies. One requirement for satisfactory performance is an effective representation of the unstructured text in a form which containstheinformationrequiredforanapplicationtask. We know of no standard methodology for evaluating word representations for technical text tailored to industry needs. In this paper, we propose guidance and methods for evaluating the performance of word representations for industrial use-cases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.