Contributions to the Improvement of Question Answering Systems in the
  Biomedical Domain
Mourad Sarrouti
This thesis work falls within the framework of question answering (QA) in the
biomedical domain where several specific challenges are addressed, such as
specialized lexicons and terminologies, the types of treated questions, and the
characteristics of targeted documents. We are particularly interested in
studying and improving methods that aim at finding accurate and short answers
to biomedical natural language questions from a large scale of biomedical
textual documents in English. QA aims at providing inquirers with direct, short
and precise answers to their natural language questions. In this Ph.D. thesis,
we propose four contributions to improve the performance of QA in the
biomedical domain. In our first contribution, we propose a machine
learning-based method for question type classification to determine the types
of given questions which enable to a biomedical QA system to use the
appropriate answer extraction method. We also propose an another machine
learning-based method to assign one or more topics (e.g., pharmacological,
test, treatment, etc.) to given questions in order to determine the semantic
types of the expected answers which are very useful in generating specific
answer retrieval strategies. In the second contribution, we first propose a
document retrieval method to retrieve a set of relevant documents that are
likely to contain the answers to biomedical questions from the MEDLINE
database. We then present a passage retrieval method to retrieve a set of
relevant passages to questions. In the third contribution, we propose specific
answer extraction methods to generate both exact and ideal answers. Finally, in
the fourth contribution, we develop a fully automated semantic biomedical QA
system called SemBioNLQA which is able to deal with a variety of natural
language questions and to generate appropriate answers by providing both exact
and ideal answers.