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

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Table 1: RDoC construct - This table shows two PubMed abstracts labelled with two different RDoC constructand PubMed ID (PMID)
Figure 1: (Left) DocNADE Topic Model: Blue colored lines signify parameter sharing, αbered arrow (1) denotes the flow.tion probability” ( attention weights areused to compute latent document representation h(v); (Right) RDoC Task-1 system architecture, wher
Figure 2: RDoC Task-2 System Architecture for Supervised and Unsupervised sentence ranking, consisting of:Query-aware Representation, Supervised and Unsupervised sentence rankers for computing the final relevancescores rsupfand runsupf, respectively
Table 2: Data statistics - # of PubMed abstracts belong-ing to each RDoC construct in different data partitions.“Loss”;Wakefulness”;(L1: “Acute Threat Fear”; L2: “Arousal”; L3: “Cir-cadian Rhythms”; L4: “Frustrative Nonreward”; L5: L6: “Potential Threat Anxiety”; L7: “Sleep L8: “Sustained Threat”)
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