Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Abir Naskar"'
Publikováno v:
Behaviour & Information Technology. 42:249-264
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ee749dfd8aff5181effb27dc4f8ba32
https://doi.org/10.1080/0144929x.2022.2139758
https://doi.org/10.1080/0144929x.2022.2139758
Text documents are rich repositories of causal knowledge. While journal publications typically contain analytical explanations of observations on the basis of scientific experiments conducted by researchers, analyst reports, News articles or even con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::138afb706f43d446a34f0b55b56e0309
https://doi.org/10.3233/ssw220021
https://doi.org/10.3233/ssw220021
Publikováno v:
Data Management, Analytics and Innovation ISBN: 9789811629365
Data Management, Analytics and Innovation
Data Management, Analytics and Innovation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98a1b115eeb95c6c53ab7d313e2daaae
https://doi.org/10.1007/978-981-16-2937-2_13
https://doi.org/10.1007/978-981-16-2937-2_13
Autor:
Tirthankar Dasgupta, Akshara Rai, Aninda Sukla, Ishan Verma, Mohammad Shakir, Lipika Dey, Tushar Goel, Abir Naskar, Suyash Sangwan
Publikováno v:
COMAD/CODS
In this paper we present a scientific document retrieval system targeted at answering specific information needs for a diverse set of professionals. This became a crying need in the wake of the pandemic when researchers, clinicians, virologists, epid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01eb8d734890aa352d53fe3d5ac9ba83
https://doi.org/10.1145/3430984.3430991
https://doi.org/10.1145/3430984.3430991
Publikováno v:
Explainable AI in Healthcare and Medicine ISBN: 9783030533519
Clinical trials are aimed to observe the effectiveness of a new intervention. For every clinical trial the eligibility requirements of a patient are specified in the form of inclusion or exclusion criteria. However, the process of eligibility determi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::febf6d84d5400ddd748bb089f5e700a5
https://doi.org/10.1007/978-3-030-53352-6_27
https://doi.org/10.1007/978-3-030-53352-6_27
Publikováno v:
COMAD/CODS
In this paper, we present the demonstration of a system that helps in analytics and visualization of crime information extracted from large text repositories. Extraction of crime indicators is performed using a CNN-BiLSTM based multi-classification n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee832ba7a1b5bce361c31f9af834fbc2
https://doi.org/10.1145/3371158.3371405
https://doi.org/10.1145/3371158.3371405
Publikováno v:
ClinicalNLP@EMNLP
Eligibility criteria in the clinical trials specify the characteristics that a patient must or must not possess in order to be treated according to a standard clinical care guideline. As the process of manual eligibility determination is time-consumi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1df2fbdacb6659a88dd314b2d26f1ddc
https://doi.org/10.18653/v1/2020.clinicalnlp-1.27
https://doi.org/10.18653/v1/2020.clinicalnlp-1.27
Publikováno v:
WI
In this paper, we have proposed natural language processing and deep learning based techniques for the automatic extraction and curation of occupational health and safety related information from safety-related articles. Such articles typically conta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ff942ee740b1876861c66b106d5f1f4
https://doi.org/10.1109/wi.2018.00-56
https://doi.org/10.1109/wi.2018.00-56
Publikováno v:
NLP-TEA@ACL
In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd0769c186bb31bc10f761b132bb60fe
https://doi.org/10.18653/v1/w18-3713
https://doi.org/10.18653/v1/w18-3713
Publikováno v:
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task.
In this paper, we have explored web-based evidence gathering and different linguistic features to automatically extract drug names from tweets and further classify such tweets into Adverse Drug Events or not. We have evaluated our proposed models wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe0785e87ab821c2227ece79b5d07adb
https://doi.org/10.18653/v1/w18-5919
https://doi.org/10.18653/v1/w18-5919