Ontology evaluation using wikipedia categories for browsing
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
Overview of the Clef 2008 multilingual question answering track
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Assessing the impact of thesaurus-based expansion techniques in QA-centric IR
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Priberam's question answering system in QA@CLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
IdSay: question answering for Portuguese
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Using answer retrieval patterns to answer Portuguese questions
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Question interpretation in QA@L²F
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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IdSay is a Question Answering system for Portuguese that participated at QA@CLEF 2008 with a baseline version (IdSayBL). Despite the encouraging results, there was still much room for improvement. The participation of six systems in the Portuguese task, with very good results either individually or in an hypothetical combination run, provided a valuable source of information. We made an analysis of all the answers submitted by all systems to identify their strengths and weaknesses. We used the conclusions of that analysis to guide our improvements, keeping in mind the two key characteristics we want for the system: efficiency in terms of response time and robustness to treat different types of data. As a result, an improved version of IdSay was developed, including as the most important enhancement the introduction of semantic information. We obtained significantly better results, from an accuracy in the first answer of 32.5% in IdSayBL to 50.5% in IdSay, without degradation of response time.