Question Answering Systems (QASs) are search engines that have the ability to provide a brief and accurate answer to each question in natural languages. The question asked in such a system is answered with a set of documents, a paragraph, a sentence, etc. In this paper, a solution is proposed to optimize the performance of web-based QASs for answering definitional and factoid questions in English. As evolutionary algorithms are suitable for issues with large search space and also texts can be examined from a variety of aspects, this approach proposes for the first time employing Multi-Objective Evolutionary algorithms (MOEAs) to optimize the performance of QASs.
In the present work, we provided a Multi-Objective QAS (MOQAS) that would be more accurate in choosing the most probable answer from the documents that the standard search engine has retrieved. Through the ranking process, various features can be extracted from the text. Each of these features examines the text from a perspective, but changes in the values of these features are not consistent with each other; thus, we need to use a method that takes all these views into account. For this purpose, three different aspects of the text are considered and NSGA-II as a MOEA is applied.
We used two standard datasets and web data for evaluating our system. Comparing the obtained results from the proposed MOQAS with other existing QASs reveals that MOQAS yields promising and effective results.
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