Search Results for author: Krishnaprasad Thirunarayan

Found 26 papers, 4 papers with code

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure

no code implementations14 Apr 2022 Ankita Agarwal, Krishnaprasad Thirunarayan, William L. Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee

Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients.

Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization

no code implementations2 Apr 2022 Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita

In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization.

Abstractive Text Summarization

Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature

1 code implementation30 Mar 2022 Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

While transformer-based encoder-decoder models in a vanilla source document-to-summary setting have been extensively studied for abstractive summarization in different domains, their major limitations continue to be entity hallucination (a phenomenon where generated summaries constitute entities not related to or present in source article(s)) and factual inconsistency.

Abstractive Text Summarization Document Summarization

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

no code implementations9 Apr 2021 Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth

In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).

The Duality of Data and Knowledge Across the Three Waves of AI

no code implementations24 Mar 2021 Amit Sheth, Krishnaprasad Thirunarayan

We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science, and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with those of symbolic AI systems, resulting in more capable AI systems that support more human-like intelligence.

Decision Making

COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study

no code implementations20 Nov 2020 Amanuel Alambo, Swati Padhee, Tanvi Banerjee, Krishnaprasad Thirunarayan

This has been exacerbated by social isolation during the pandemic and the social stigma associated with mental health and substance use disorders, making people reluctant to share their struggles and seek help.

Predicting Early Indicators of Cognitive Decline from Verbal Utterances

no code implementations19 Nov 2020 Swati Padhee, Anurag Illendula, Megan Sadler, Valerie L. Shalin, Tanvi Banerjee, Krishnaprasad Thirunarayan, William L. Romine

Our experiments show that a combination of contextual and psycholinguistic features extracted by a Support Vector Machine improved distinguishing the verbal utterances of elderly controls, people with MCI, possible AD, and probable AD.

Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles

no code implementations3 Nov 2020 Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis.

Abstractive Text Summarization Document Summarization +6

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction

no code implementations21 Sep 2020 Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression.

Association

Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak

no code implementations30 Jul 2020 Amanuel Alambo, Manas Gaur, Krishnaprasad Thirunarayan

Further, apart from providing informative content to the public, the incessant media coverage of COVID-19 crisis in terms of news broadcasts, published articles and sharing of information on social media have had the undesired snowballing effect on stress levels (further elevating depression and drug use) due to uncertain future.

Informativeness Semantic Parsing +1

Towards Geocoding Spatial Expressions

no code implementations12 Jun 2019 Hussein S. Al-Olimat, Valerie L. Shalin, Krishnaprasad Thirunarayan, Joy Prakash Sain

Imprecise composite location references formed using ad hoc spatial expressions in English text makes the geocoding task challenging for both inference and evaluation.

Translation

Fusing Visual, Textual and Connectivity Clues for Studying Mental Health

no code implementations19 Feb 2019 Amir Hossein Yazdavar, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amirhassan Monadjemi, Krishnaprasad Thirunarayan, Amit Sheth, Jyotishman Pathak

With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media.

Analyzing and learning the language for different types of harassment

no code implementations1 Nov 2018 Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth

In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political.

A Practical Incremental Learning Framework For Sparse Entity Extraction

1 code implementation COLING 2018 Hussein S. Al-Olimat, Steven Gustafson, Jason Mackay, Krishnaprasad Thirunarayan, Amit Sheth

This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.

Active Learning Entity Extraction using GAN +1

Predictive Analysis on Twitter: Techniques and Applications

1 code implementation6 Jun 2018 Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide.

Social and Information Networks

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

no code implementations26 Feb 2018 Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth

In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment. We crawled data from Twitter using our offensive lexicon.

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

no code implementations16 Oct 2017 Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter.

Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models

1 code implementation COLING 2018 Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth

Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names.

Language Modelling

Implicit Entity Linking in Tweets

no code implementations26 Jul 2017 Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit Sheth, Krishnaprasad Thirunarayan

We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book.

Entity Linking Natural Language Understanding

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

no code implementations14 Jul 2017 Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan

Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation

no code implementations19 Jan 2017 Saeedeh Shekarpour, Faisal Al-Shargi, Valerie Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth

These use-cases demonstrate the benefits of using CEVO for annotation: (i) annotating English verbs from an abstract conceptualization, (ii) playing the role of an upper ontology for organizing ontological properties, and (iii) facilitating the annotation of text relations using any underlying vocabulary.

On Reasoning with RDF Statements about Statements using Singleton Property Triples

no code implementations15 Sep 2015 Vinh Nguyen, Olivier Bodenreider, Krishnaprasad Thirunarayan, Gang Fu, Evan Bolton, Núria Queralt Rosinach, Laura I. Furlong, Michel Dumontier, Amit Sheth

If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?

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