Search for Answers in Domain-Specific Supported by Intelligent Agents

Search for answers in specific domains is a new milestone in question answering. Traditionally, question answering has focused on general domain questions. Thus, the most relevant answers (or passages) are selected according to the type of question and the Named Entities included in the possible answers. In this paper, we present a novel approach on question answering over specific (or technical) domains. This proposal allows us to answer questions such as “What article is appropriate for … “, “What are the articles related to … “, these kind of questions cannot be answered by a general question answering system. Our approach is based on a set of laws of a specific domain, which contain a large set of laws regarding the work organized into a hierarchy. We consider generic concepts such as “article” semantic categories. Our results on the corpus of Federal Labor Law show that this approach is effective and highly reliable.