Towards Data And Goal Oriented Analysis: Tool Inter-Operability And Combinatorial Comparison
Yoshinobu Kano, Ngan Luu-Thuy Nguyen, Rune Sætre, Kazuhiro YOSHIDA, K Fukamachi, Yusuke Miyao, Yoshimasa Tsuruoka, Sophia Ananiadou, Jun’ichi Tsujii · Research Explorer (The University of Manchester) · 2008
Recently, NLP researches have advanced using F-scores, precisions, and recalls with gold standard data as evaluation measures. However, such evaluations cannot capture the different behaviors of varying NLP tools or the different behaviors of a NLP tool that depends on the data and domain in which it works. Because an increasing number of tools are available nowadays, it has become increasingly important to grasp these behavioral differences, in order to select a suitable set of tools, which forms a complex workflow for a specific purpose. In order to observe such differences, we need to integrate available combinations of tools into a workflow and to compare the combinatorial results. Although generic frameworks like UIMA (Unstructured Information Management Architecture) provide interoperability to solve this problem, the solution they provide is only partial. In order for truly interoperable toolkits to become a reality, we also need sharable and comparable type systems with an automatic combinatorial comparison generator, which would allow systematic comparisons of available tools. In this paper, we describe such an environment, which we developed based on UIMA, and we show its feasibility through an example of a protein-protein interaction (PPI) extraction system. 1