Answering Factoid Questions in the Biomedical Domain

In this work we present a novel approach towards the extraction of factoid answers to biomedical questions. The approach is based on the combination of structured (ontological) and unstructured (textual) knowledge sources, which enables the system to extract factoid answer candidates out of a predefined set of documents that are related to the input questions. The candidates are scored by applying a variety of scoring schemes and are combined to find the best extracted candidate answer. The suggested approach was submitted in the framework of the BioASQ challenge as the baseline system to address the automated answering of factoid questions, in the framework of challenge 1b. Preliminary evaluation in the factoid questions of the dry-run set of the competition shows promising results, with a reported average accuracy of 54.66%.

[1]  Feng Xia,et al.  Introduction to , 2015, ACM Trans. Multim. Comput. Commun. Appl..

[2]  Axel-Cyrille Ngonga Ngomo,et al.  BioASQ: A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering , 2012, AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text.

[3]  Hyoil Han,et al.  Biomedical question answering: A survey , 2010, Comput. Methods Programs Biomed..

[4]  Jimmy J. Lin,et al.  Answering Clinical Questions with Knowledge-Based and Statistical Techniques , 2007, CL.

[5]  Aditya Kalyanpur,et al.  Typing candidate answers using type coercion , 2012, IBM J. Res. Dev..

[6]  David A. Ferrucci,et al.  Introduction to "This is Watson" , 2012, IBM J. Res. Dev..

[7]  Mengqiu Wang,et al.  A Survey of Answer Extraction Techniques in Factoid Question Answering , 2006 .

[8]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .