54 citations · 84 across the 21 of their papers we have counts for
21 papers
ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language
Zhaoyue Sun, Jiazheng Li, Gabriele Pergola +1
Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or p…
DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information
Artem Bobrov, Domantas Saltenis, Zhaoyue Sun +2
Drug safety research is crucial for maintaining public health, often requiring comprehensive data support. However, the resources currently available to the public are limited and…
Cascading Large Language Models for Salient Event Graph Generation
Xingwei Tan, Yuxiang Zhou, Gabriele Pergola +1
Generating event graphs from long documents is challenging due to the inherent complexity of multiple tasks involved such as detecting events, identifying their relationships, and…
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation
Xingwei Tan, Yuxiang Zhou, Gabriele Pergola +1
Event temporal graphs have been shown as convenient and effective representations of complex temporal relations between events in text. Recent studies, which employ pre-trained lan…
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study
Zhaoyue Sun, Gabriele Pergola, Byron C. Wallace +1
With the advent of large language models (LLMs), there has been growing interest in exploring their potential for medical applications. This research aims to investigate the abilit…
MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
Junru Lu, Siyu An, Mingbao Lin +5
We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range…
Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering
Junru Lu, Gabriele Pergola, Lin Gui +1
We propose a simple yet effective strategy to incorporate event knowledge extracted from event trigger annotations via posterior regularization to improve the event reasoning capab…
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
Junru Lu, Jiazheng Li, Byron C. Wallace +2
Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potenti…
Event Temporal Relation Extraction with Bayesian Translational Model
Xingwei Tan, Gabriele Pergola, Yulan He
Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge. In this study, we introduce Bayesian-Trans, a Bayesian lear…
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
Zhaoyue Sun, Jiazheng Li, Gabriele Pergola +5
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimatel…
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation
Junru Lu, Xingwei Tan, Gabriele Pergola +2
Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, w…
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media
Lixing Zhu, Zheng Fang, Gabriele Pergola +2
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated dat…
Extracting Event Temporal Relations via Hyperbolic Geometry
Xingwei Tan, Gabriele Pergola, Yulan He
Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map…
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
Lixing Zhu, Gabriele Pergola, Lin Gui +2
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intr…
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction
Hanqi Yan, Lin Gui, Gabriele Pergola +1
The Emotion Cause Extraction (ECE)} task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-use…
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
Gabriele Pergola, Elena Kochkina, Lin Gui +2
Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature. Although an i…
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews
Runcong Zhao, Lin Gui, Gabriele Pergola +1
In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentime…
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering
Junru Lu, Gabriele Pergola, Lin Gui +2
We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting…
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews
Gabriele Pergola, Lin Gui, Yulan He
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (…
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context
Gabriele Pergola, Yulan He, David Lowe
Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus. Several topic model…
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis
Gabriele Pergola, Lin Gui, Yulan He
We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and emplo…