Search Results for author:
Found 6 papers, 0 papers with code
Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria
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.
ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction
We describe our submission to the FinCausal 2022 shared task based on this method.
Leveraging Web Based Evidence Gathering for Drug Information Identification from Tweets
Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78. 5{\%}, 88{\%} and 82. 9{\%} respectively for Task1 and 33. 2{\%}, 54. 7{\%} and 41. 3{\%} for Task3.
Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports
We have done experiments with a collection of 5000 crime-reporting News articles span over time, and multiple sources.
Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring
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.
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks
In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text.
