ESTIMATION QUESTION TYPE ANALYZER FOR MULTI CLOSE DOMAIN INDONESIAN QAS

Abstract

We propose an automated estimation scheme to analyze question classification in Indonesian multi closed domain question answering systems. The goal is to provide a good questioning classification system even if using only available language sources. Our strategy here is to build a pattern and rule to extract some important words and utilize the results as a feature for classification estimation of automated learning-based questions. Scenarios designed in automated learning estimates: (i) Analyzing questions, to represent the key information needed to answer user questions using target focus and target identification; (ii) Classify the type of question, construct a taxonomy of questions that have been coded into the system to determine the expected answer type, through some question processing patterns and rules. The proposed method is evaluated using datasets collected from various Indonesian websites. Test results show that the classification process using the proposed method is very effective.