TEST: A Terminology Extraction System for Technology Related Terms
This addresses the challenge for AI researchers and practitioners in keeping up with rapidly evolving technologies, though it appears incremental as it builds on existing extraction methods.
The paper tackles the problem of tracking new technologies in AI by proposing a two-stage system for automatically detecting and extracting technology-related terms from text, achieving competitive accuracy in both sentence classification and keyword identification.
Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified into the relevant semantic labels and AI domains. Our proposed tool is based on a two-stage cascading model -- the first stage classifies if the sentence contains a technology term or not; and the second stage identifies the technology keyword in the sentence. We obtain a competitive accuracy for both tasks of sentence classification and text identification.