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This paper qualifies what a true termrecognition systems would have to recognize. The exact bracketing of the maximal termform is then proposed as an achieveable goal upon which current system performance should be measured. How recall and precision metrics are best adapted for measuring term recognition is suggested. derlying premises should be made clear. Firstly, the automatic system is designed to recognize segments of text that, conventionally, have been manually identified by a terminologist, indexer, lexicographer or other trained individual. Secondly, the performance of automatic term-recognition systems is best measured against human performance for the same task. . | Criteria for Measuring Term Recognition Andy Lauriston Department of Languages and Linguistics University of Manchester Institute of Science and Technology P.O. Box 88 Manchester M60 1QD United Kingdom andyl@ccl.umist.ac.uk Abstract This paper qualifies what a true termrecognition systems would have to recognize. The exact bracketing of the maximal termform is then proposed as an achieveable goal upon which current system performance should be measured. How recall and precision metrics are best adapted for measuring term recognition is suggested. 1 Introduction In recent years the automatic extraction of terms from running text has become a subject of growing interest. Practical applications such as dictionary lexicon and thesaurus construction and maintenance automatic indexing and machine translation have fuelled this interest. Given that concerns in automatic term recognition are practical rather than theoretical the lack of serious performance measurements in the published literature is surprising. Accounts of term-recognition systems sometimes consist of a purely descriptive statement of the advantages of a particular approach and make no attempt to measure the pay-off the proposed approach yields David 1990 . Others produce partial figures without any clear statement of how they are derived Otman 1991 . One of the best efforts to quantify the performance of a term-recognition system Smadja 1993 does so only for one processing stage leaving unassessed the text-to-output performance of the system. While most automatic term-recognition systems developed to date have been experimental or inhouse ones a few systems like TermCruncher Normand 1993 are now being marketed. Both the developers and users of such systems would benefit greatly by clearly qualifying what each system aims to achieve and precisely quantifying how closely the system comes to achieving its stated aim. Before discussing what a term-recognition system should be expected to recognize and how .
