A Joint Local-Global Approach for Medical Terminology Assignment

In community-based health services, vocabulary gap between health seekers and community generated knowledge has hindered data access. To bridge this gap, this paper presents a scheme to label question answer(QA) pairs by jointly utilizing local mining and global learning approaches. Local mining attempts to label individual QA pair by independently extracting medical concepts from the QA pair itself and mapping them to authenticated terminologies. However, it may suffer from information loss and lower precision, which are caused by the absence of key medical concepts and presence of irrelevant medical concepts. Global learning, on the other hand, works towards enhancing the local mining via collaboratively discovering missing key terminologies and keeping off the irrelevant terminologies by analyzing the social neighbors. Practically, this unsupervised scheme holds potential to large-scale data.