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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.
Identifying pandemic-related stress factors from social-media posts – Effects on students and young-adults
The COVID-19 pandemic has thrown natural life out of gear across the globe.
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.
Learning Domain Terms - Empirical Methods to Enhance Enterprise Text Analytics Performance
Performance of standard text analytics algorithms are known to be substantially degraded on consumer generated data, which are often very noisy.
Identification, Tracking and Impact: Understanding the trade secret of catchphrases
Understanding the topical evolution in industrial innovation is a challenging problem.
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.
TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture
This paper presents system description of our submission to the SemEval-2018 task-1: Affect in tweets for the English language.
A Machine Learning Approach to Quantitative Prosopography
Prosopography is an investigation of the common characteristics of a group of people in history, by a collective study of their lives.
Multi-Document Summarization using Distributed Bag-of-Words Model
As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time.
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets
This paper describes our approach to the Emotion Intensity shared task.
A Framework for Mining Enterprise Risk and Risk Factors from News Documents
Any real world events or trends that can affect the company{'}s growth trajectory can be considered as risk.
