English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This thesis work falls within the framework of question answering (QA) in thebiomedical domain where several specific challenges are addressed, such asspecialized lexicons and terminologies, the types of treated questions, and thecharacteristics of targeted documents. We are particularly interested instudying and improving methods that aim at finding accurate and short answersto biomedical natural language questions from a large scale of biomedicaltextual documents in English. QA aims at providing inquirers with direct, shortand precise answers to their natural language questions. In this Ph.D. thesis,we propose four contributions to improve the performance of QA in thebiomedical domain. In our first contribution, we propose a machinelearning-based method for question type classification to determine the typesof given questions which enable to a biomedical QA system to use theappropriate answer extraction method. We also propose an another machinelearning-based method to assign one or more topics (e.g., pharmacological,test, treatment, etc.) to given questions in order to determine the semantictypes of the expected answers which are very useful in generating specificanswer retrieval strategies. In the second contribution, we first propose adocument retrieval method to retrieve a set of relevant documents that arelikely to contain the answers to biomedical questions from the MEDLINEdatabase. We then present a passage retrieval method to retrieve a set ofrelevant passages to questions. In the third contribution, we propose specificanswer extraction methods to generate both exact and ideal answers. Finally, inthe fourth contribution, we develop a fully automated semantic biomedical QAsystem called SemBioNLQA which is able to deal with a variety of naturallanguage questions and to generate appropriate answers by providing both exactand ideal answers.

Contributions to the Improvement of Question Answering Systems in the Biomedical Domain