Question and Answer Classification in Czech Question Answering Benchmark Dataset
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 11th International Conference on Agents and Artificial Intelligence, Volume 2 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.5220/0007396907010706 |
| Keywords | Question Answering; Question Classification; Answer Classification; Czech; Simple Question Answering Database; SQAD |
| Description | 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. |
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