A comparative evaluation of term recognition algorithms

Automatic Term Recognition (ATR) is a fundamental processing step preceding more complex tasks such as semantic search and ontology learning. From a large number of methodologies available in the literature only a few are able to handle both single and multi-word terms. In this paper we present a comparison of five such algorithms and propose a combined approach using a voting mechanism. We evaluated the six approaches using two different corpora and show how the voting algorithm performs best on one corpus (a collection of texts from Wikipedia) and less well using the Genia corpus (a standard life science corpus). This indicates that choice and design of corpus has a major impact on the evaluation of term recognition algorithms. Our experiments also showed that single-word terms can be equally important and occupy a fairly large proportion in certain domains. As a result, algorithms that ignore single-word terms may cause problems to tasks built on top of ATR. Effective ATR systems also need to take into account both the unstructured text and the structured aspects and this means information extraction techniques need to be integrated into the term recognition process.

Measuring performance of virtual learning environment system in higher education

Purpose – The purpose of this paper is to measure the performance of commercial virtual learning environment (VLE) systems, which helps the decision makers to select the appropriate system for their institutions. Design/methodology/approach – This paper develops an integrated multiple criteria decision making approach, which combines the analytic hierarchy process (AHP) and quality function deployment (QFD), to evaluate and select the best system. The evaluating criteria are derived from the requirements of those who use the system. A case study is provided to demonstrate how the integrated approach works. Findings – The major advantage of the integrated approach is that the evaluating criteria are of interest to the stakeholders. This ensures that the selected system will achieve the requirements and satisfy the stakeholders most. Another advantage is that the approach can guarantee the benchmarking to be consistent and reliable. From the case study, it is proved that the performance of a VLE system being used at the university is the best. Therefore, the university should continue to run the system in order to support and facilitate both teaching and learning. Originality/value – It is believed that there is no study that measures the performance of VLE systems, and thus decision makers may have difficulties in system evaluation and selection for their institutions.