86 citations · 394 across the 25 of their papers we have counts for
24 papers · 1 filter
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
Tomas Goldsack, Zheheng Luo, Qianqian Xie +4
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this sh…
A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge
Kailai Yang, Tianlin Zhang, Shaoxiong Ji +1
The context-aware emotional reasoning ability of AI systems, especially in conversations, is of vital importance in applications such as online opinion mining from social media and…
Graph Contrastive Topic Model
Zheheng Luo, Lei Liu, Qianqian Xie +1
Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with sim…
Disentangled Variational Autoencoder for Emotion Recognition in Conversations
Kailai Yang, Tianlin Zhang, Sophia Ananiadou
In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the r…
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
Shaoxiong Ji, Tianlin Zhang, Kailai Yang +3
Pretrained language models have been used in various natural language processing applications. In the mental health domain, domain-specific language models are pretrained and relea…
Emotion fusion for mental illness detection from social media: A survey
Tianlin Zhang, Kailai Yang, Shaoxiong Ji +1
Mental illnesses are one of the most prevalent public health problems worldwide, which negatively influence people's lives and society's health. With the increasing popularity of s…
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization
Zheheng Luo, Qianqian Xie, Sophia Ananiadou
The performance of text summarization has been greatly boosted by pre-trained language models. A main concern of existing methods is that most generated summaries are not factually…
Zero-shot Temporal Relation Extraction with ChatGPT
Chenhan Yuan, Qianqian Xie, Sophia Ananiadou
The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investi…
A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models
Qianqian Xie, Zheheng Luo, Benyou Wang +1
The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), poses a significant challenge for clinicians and researchers to acces…
Span-based Named Entity Recognition by Generating and Compressing Information
Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou
The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression…
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations
Kailai Yang, Tianlin Zhang, Hassan Alhuzali +1
A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses…
Readability Controllable Biomedical Document Summarization
Zheheng Luo, Qianqian Xie, Sophia Ananiadou
Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of bio…
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization
Qianqian Xie, Jimin Huang, Tulika Saha +1
Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization. However, in…
Learning Disentangled Representations of Negation and Uncertainty
Jake Vasilakes, Chrysoula Zerva, Makoto Miwa +1
Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically…
Investigating Text Simplification Evaluation
Laura Vásquez-Rodríguez, Matthew Shardlow, Piotr Przybyła +1
Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models. However, existing studies show that parallel TS corpora c…
EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature
Jiarun Cao, Elke M van Veen, Niels Peek +2
To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Name…
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Kno…
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
Hassan Alhuzali, Sophia Ananiadou
Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling…
Revisiting Unsupervised Relation Extraction
Thy Thy Tran, Phong Le, Sophia Ananiadou
Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be…
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection
Kurt Espinosa, Makoto Miwa, Sophia Ananiadou
We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search proble…
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text. Existing approaches use graph…
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa +1
Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing…
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks
Maolin Li, Arvid Fahlström Myrman, Tingting Mu +1
When constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide…
A Walk-based Model on Entity Graphs for Relation Extraction
Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All…