Authors:
Daša Kušniráková
;
Marek Medved
and
Aleš Horák
Affiliation:
Natural Language Processing Centre, Faculty of Informatics, Masaryk University Botanická 68a, 602 00, Brno and Czech Republic
Keyword(s):
Question Answering, Question Classification, Answer Classification, Czech, Simple Question Answering Database, SQAD.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Applications
;
Artificial Intelligence
;
Conversational Agents
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
Abstract:
In this paper, we introduce a new updated version of the Czech Question Answering database SQAD v2.1 (Simple Question Answering Database) with the update being devoted to improved question and answer classification. The SQAD v2.1 database contains more than 8,500 question-answer pairs with all appropriate metadata for QA training and evaluation. We present the details and changes in the database structure as well as a new algorithm for detecting the question type and the actual answer type from the text of the question. The algorithm is evaluated with more than 4,000 question answer pairs reaching the F1-measure of 88% for question typed and 85% for answer type detection.