Automatic Term Extraction in Technical Domain using Part-of-Speech and Common-Word Features

Abstract

Extracting key terms from technical documents allows us to write effective documentation that is specific and clear, with minimum ambiguity and confusion caused by nearly synonymous but different terms. For instance, in order to avoid confusion, the same object should not be referred to by two different names (e.g. "hydraulic oil filter"). In the modern world of commerce, clear terminology is the hallmark of successful RFPs (Requests for Proposal) and is therefore a key to the growth of competitive organizations. While Automatic Term Extraction (ATE) is a well-developed area of study, its applications in the technical domain have been sparse and constrained to certain narrow areas such as the biomedical research domain. We present a method for Automatic Term Extraction (ATE) for the technical domain based on the use of part-of-speech features and common words information. The method is evaluated on a C programming language reference manual as well as a manual of aircraft maintenance guidelines, and has shown comparable or better results to the reported state of the art results.