C4.5: programs for machine learning
C4.5: programs for machine learning
AnswerBus question answering system
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Overview of the CLEF 2007 Multilingual Question Answering Track
Advances in Multilingual and Multimodal Information Retrieval
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
Overview of the CLEF 2005 multilingual question answering track
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Priberam’s question answering system for portuguese
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
A pattern learning approach to question answering within the ephyra framework
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Overview of the CLEF 2004 multilingual question answering track
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
First evaluation of esfinge: a question answering system for portuguese
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Overview of the CLEF 2006 multilingual question answering track
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
A first step to address biography generation as an iterative QA task
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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The task of a Question Answering (QA) system is to automatically answer a question in natural language, searching for information in a given data source, such as structured databases or unstructured natural language documents. In this paper we investigate how much is needed in terms of tools and resources for a QA system for Brazilian Portuguese. In particular we assess the impact of shallow and deep methods and the contribution of different tools and resources in terms of the performance of the system in the task. We argue that a combination of deep and shallow strategies results in optimal performance, but that shallow methods alone may already give adequate results.