Titlebook: Recent Trends in Materials and Devices; Proceedings of ICRTM Vinod Kumar Jain,Sunita Rattan,Abhishek Verma Conference proceedings 2020 The

Engulf

Adarsh Kumarith concepts from the UMLS, and split in an equally-sized training and test set. The best performance on the training set was obtained with a terminology that contained the intersection of the translated terms in combination with several post-processing steps to reduce the number of false-positive d

膽小鬼

Ch. Kartikeshwar Patro,Aakarti Garg,Rohit Verma,Ravindra Dhar,Roman Dabrowskined by personal phrases considerably outperformed those from non-personal sentences, indicating their greater suitability for the AP task. We consider these findings could be further applied in the design of strategies for the construction of AP corpora, novel feature selection methods, as well as n

一致性

Shivani A. Kumar,H. Prakash,N. Chandra,R. Prakashned by personal phrases considerably outperformed those from non-personal sentences, indicating their greater suitability for the AP task. We consider these findings could be further applied in the design of strategies for the construction of AP corpora, novel feature selection methods, as well as n

連詞

過(guò)濾

Headstrong

Adarsh Kumart focus on the fine-grained recognition still lacks. We revisit the previously unfruitful neural approaches to improve recognition performance for the fine-grained entities. In this paper, we test the feasibility and quality of multitask learning (MTL) to improve fine-grained PICO recognition using

結(jié)合

Arable

Satendra Kumar,Rohit Verma,Ravindra Dhart focus on the fine-grained recognition still lacks. We revisit the previously unfruitful neural approaches to improve recognition performance for the fine-grained entities. In this paper, we test the feasibility and quality of multitask learning (MTL) to improve fine-grained PICO recognition using

PANEL

Navshad Alam,Tahira Khatoon,Vishal Singh Chandel,Rashmiduces a framework to reuse and customise existing real-life data collections. The framework outlines the eligibility criteria and the data structure requirements needed for this task. It also details the process to transform the data into a ground-truth dataset. We apply this framework to two existi

Servile