Abstract
One of the most accurate methods in Question Answering (QA) uses off-line information extraction to find answers for frequently asked questions. It requires automatic extraction from text of all relation instances for relations that users frequently ask for. In this chapter, two methods are presented for learning relation instances for relations relevant in a closed and open domain (medical) QA system. Both methods try to learn automatic dependency paths that typically connect two arguments of a given relation. The first (lightly supervised) method starts from a seed list of argument instances, and extracts dependency paths from all sentences in which a seed pair occurs. This method works well for large text collections and for seeds which are easily identified, such as named entities, and is well-suited for open domain QA. A second experiment concentrates on medical relation extraction for the question answering module of the IMIX system. The IMIX corpus is relatively small and relation instances may contain complex noun phrases that do not occur frequently in the exact same form in the corpus. In this case, learning from annotated data is necessary. Dependency patterns enriched with semantic concept labels are shown to give accurate results for relations that are relevant for a medical QA system. Both methods improve the performance of the Dutch QA system Joost.
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Bouma, G., Fahmi, I., Mur, J. (2011). Relation Extraction for Open and Closed Domain Question Answering. In: van den Bosch, A., Bouma, G. (eds) Interactive Multi-modal Question-Answering. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17525-1_8
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