On Evaluation of Natural Language Processing Tasks: Is Gold Standard Evaluation Methodology a Good Solution?
|Year of publication
|Article in Proceedings
|Proceedings of the 8th International Conference on Agents and Artificial Intelligence
|MU Faculty or unit
|Natural Language Processing; Applications; Evaluation
|The paper discusses problems in state of the art evaluation methods used in natural language processing (NLP). Usually, some form of gold standard data is used for evaluation of various NLP tasks, ranging from morphological annotation to semantic analysis. We discuss problems and validity of this type of evaluation, for various tasks, and illustrate the problems on examples. Then we propose using application-driven evaluations, wherever it is possible. Although it is more expensive, more complicated and not so precise, it is the only way to find out if a particular tool is useful at all.