Search Results for author: Paul Thompson

Found 23 papers, 2 papers with code

ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model

1 code implementation11 Mar 2024 Zhiwei Liu, Boyang Liu, Paul Thompson, Kailai Yang, Raghav Jain, Sophia Ananiadou

Driven by a comprehensive analysis of conspiracy text that reveals its distinctive affective features, we propose ConspEmoLLM, the first open-source LLM that integrates affective information and is able to perform diverse tasks relating to conspiracy theories.

Binary Classification Language Modelling +2

Emotion Detection for Misinformation: A Review

no code implementations1 Nov 2023 Zhiwei Liu, Tianlin Zhang, Kailai Yang, Paul Thompson, Zeping Yu, Sophia Ananiadou

The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors.

Fake News Detection Misinformation

Towards Sparsified Federated Neuroimaging Models via Weight Pruning

no code implementations24 Aug 2022 Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar, Greg Ver Steeg, Paul Thompson, José Luis Ambite

In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions.

Federated Learning

Functional2Structural: Cross-Modality Brain Networks Representation Learning

no code implementations6 May 2022 Haoteng Tang, Xiyao Fu, Lei Guo, Yalin Wang, Scott Mackin, Olusola Ajilore, Alex Leow, Paul Thompson, Heng Huang, Liang Zhan

Since brain networks derived from functional and structural MRI describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks is non-trivial.

Disease Prediction Graph Learning +2

HSEarch: semantic search system for workplace accident reports

no code implementations23 Mar 2021 Emrah Inan, Paul Thompson, Tim Yates, Sophia Ananiadou

Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of finding relevant documents and locating important information within them.

Deep Representation Learning For Multimodal Brain Networks

no code implementations19 Jul 2020 Wen Zhang, Liang Zhan, Paul Thompson, Yalin Wang

The higher-order network mappings from brain structural networks to functional networks are learned in the node domain.

Anatomy Graph Representation Learning

Semantic Annotation for Improved Safety in Construction Work

no code implementations LREC 2020 Paul Thompson, Tim Yates, Emrah Inan, Sophia Ananiadou

In response, we have designed a novel named entity annotation scheme and associated guidelines for this domain, which covers hazards, consequences, mitigation strategies and project attributes.

Management named-entity-recognition +2

Large-Scale Unsupervised Deep Representation Learning for Brain Structure

no code implementations2 May 2018 Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul Thompson

Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data.

Representation Learning

Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure

no code implementations12 Sep 2017 Tao Yang, Paul Thompson, Sihai Zhao, Jieping Ye

As a regression model, it is competitive to the state-of-the-arts sparse models; as a variable selection method, SGLGG is promising for identifying Alzheimer's disease-related risk SNPs.

Variable Selection

Structural Connectome Validation Using Pairwise Classification

no code implementations26 Jan 2017 Dmitry Petrov, Boris Gutman, Alexander Ivanov, Joshua Faskowitz, Neda Jahanshad, Mikhail Belyaev, Paul Thompson

In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains.

Classification General Classification

Identifying Content Types of Messages Related to Open Source Software Projects

no code implementations LREC 2016 Yannis Korkontzelos, Paul Thompson, Sophia Ananiadou

Assessing the suitability of an Open Source Software project for adoption requires not only an analysis of aspects related to the code, such as code quality, frequency of updates and new version releases, but also an evaluation of the quality of support offered in related online forums and issue trackers.

Identification of Manner in Bio-Events

no code implementations LREC 2012 Raheel Nawaz, Paul Thompson, Sophia Ananiadou

Until recently, these corpora, and hence the event extraction systems trained on them, focussed almost exclusively on the identification and classification of event arguments, without taking into account how the textual context of the events could affect their interpretation.

Event Extraction

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