COALA - A Rule-Based Approach to Answer Type Prediction

Nadine Steinmetz, Kai-Uwe Sattler
2020 International Semantic Web Conference  
For answering a question correctly, the previous detection of the answer type is essential. Especially in the field of Question Answering (QA) over knowledge bases, answers might be of many different types as natural language is ambiguous and a question might lead to different relevant queries. For semantic knowledge bases data types (such as date, string, or number) as well as all ontology classes (such as athlete, championship, or television show) have to be taken into account. Therefore, the
more » ... previous detection of the answer type is a helpful sub-task for QA systems, but also a complex classification problem. We present our rulebased approach COntext Aware anaLysis of Answer types (COALA). Our approach is based on the extraction of several question features and the context aware disambiguation to retrieve the correct answer type. COALA has been developed in the course of the SMART task challenge and we evaluated our approach based on over 21,000 questions.
dblp:conf/semweb/SteinmetzS20 fatcat:rbmg2kho6ncjzaxocfjbchg43e