BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions

Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze


Abstract
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 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively.
Anthology ID:
D19-5730
Volume:
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
227–236
Language:
URL:
https://aclanthology.org/D19-5730
DOI:
10.18653/v1/D19-5730
Bibkey:
Cite (ACL):
Pankaj Gupta, Yatin Chaudhary, and Hinrich Schütze. 2019. BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 227–236, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions (Gupta et al., BioNLP 2019)
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PDF:
https://aclanthology.org/D19-5730.pdf
Code
 YatinChaudhary/RDoC_Task