  This paper presents our system details and results of participation in the
RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a
multi-dimensional and broad framework to describe mental health disorders by
combining knowledge from genomics to behaviour. Non-availability of RDoC
labelled dataset and tedious labelling process hinders the use of RDoC
framework to reach its full potential in Biomedical research community and
Healthcare industry. Therefore, Task-1 aims at retrieval and ranking of PubMed
abstracts relevant to a given RDoC construct and Task-2 aims at extraction of
the most relevant sentence from a given PubMed abstract. We investigate (1)
attention based supervised neural topic model and SVM for retrieval and ranking
of PubMed abstracts and, further utilize BM25 and other relevance measures for
re-ranking, (2) supervised and unsupervised sentence ranking models utilizing
multi-view representations comprising of query-aware attention-based sentence
representation (QAR), bag-of-words (BoW) and TF-IDF. Our best systems achieved
1st rank and scored 0.86 mean average precision (mAP) and 0.58 macro average
accuracy (MAA) in Task-1 and Task-2 respectively.
