Search Results for author: Sophia Ananiadou

Found 88 papers, 21 papers with code

RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification

no code implementations LREC 2022 Annika Marie Schoene, Nina Dethlefs, Sophia Ananiadou

These resources are either large, common-sense knowledge graphs (KG) that cover a limited amount of polarities/emotions or they are smaller in size (e. g.: lexicons), which require costly human annotation and cover fine-grained emotions.

Common Sense Reasoning Emotion Classification +2

Incorporating Zoning Information into Argument Mining from Biomedical Literature

no code implementations LREC 2022 Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, Sophia Ananiadou

Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers.

Argument Mining

Text Classification and Prediction in the Legal Domain

no code implementations LREC 2022 Minh-Quoc Nghiem, Paul Baylis, André Freitas, Sophia Ananiadou

We present a case study on the application of text classification and legal judgment prediction for flight compensation.

Management text-classification +1

GenCompareSum: a hybrid unsupervised summarization method using salience

1 code implementation BioNLP (ACL) 2022 Jennifer Bishop, Qianqian Xie, Sophia Ananiadou

To this end, we propose a hybrid, unsupervised, abstractive-extractive approach, in which we walk through a document, generating salient textual fragments representing its key points.

Extractive Summarization Text Summarization

Rethinking Large Language Models in Mental Health Applications

no code implementations19 Nov 2023 Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria

Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications.

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

Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models

1 code implementation2 Oct 2023 Chenhan Yuan, Qianqian Xie, Jimin Huang, Sophia Ananiadou

In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction.

Instruction Following Temporal Relation Extraction

Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles

no code implementations29 Sep 2023 Tomas Goldsack, Zheheng Luo, Qianqian Xie, Carolina Scarton, Matthew Shardlow, Sophia Ananiadou, Chenghua Lin

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.

Lay Summarization

LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation

1 code implementation21 Sep 2023 Jennifer A Bishop, Qianqian Xie, Sophia Ananiadou

This framework outperforms existing state-of-the-art metrics in its ability to correlate with human measures of factuality when used to evaluate long document summarisation data sets.

A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge

1 code implementation9 Aug 2023 Kailai Yang, Tianlin Zhang, Shaoxiong Ji, Sophia Ananiadou

However, most previous knowledge infusion methods perform empirical knowledge filtering and design highly customized architectures for knowledge interaction with the utterances, which can discard useful knowledge aspects and limit their generalizability to different knowledge sources.

Opinion Mining

Graph Contrastive Topic Model

1 code implementation5 Jul 2023 Zheheng Luo, Lei Liu, Qianqian Xie, Sophia Ananiadou

Based on it, we propose the graph contrastive topic model (GCTM), which conducts graph contrastive learning (GCL) using informative positive and negative samples that are generated by the graph-based sampling strategy leveraging in-depth correlation and irrelevance among documents and words.

Contrastive Learning Representation Learning

Disentangled Variational Autoencoder for Emotion Recognition in Conversations

1 code implementation23 May 2023 Kailai Yang, Tianlin Zhang, Sophia Ananiadou

We also enhance the disentangled representations by introducing VAD supervision signals from a sentiment lexicon and minimising the mutual information between VAD distributions.

Emotion Recognition Response Generation

Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health

no code implementations20 Apr 2023 Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria, Jörg Tiedemann

In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions.

Emotion fusion for mental illness detection from social media: A survey

no code implementations19 Apr 2023 Tianlin Zhang, Kailai Yang, Shaoxiong Ji, Sophia Ananiadou

In this article, we provide a comprehensive survey of approaches to mental illness detection in social media that incorporate emotion fusion.

A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models

no code implementations18 Apr 2023 Qianqian Xie, Zheheng Luo, Benyou Wang, Sophia Ananiadou

In this paper, we present a systematic review of recent advancements in BTS, leveraging cutting-edge NLP techniques from PLMs to LLMs, to help understand the latest progress, challenges, and future directions.

Information Retrieval Language Modelling +3

Zero-shot Temporal Relation Extraction with ChatGPT

no code implementations11 Apr 2023 Chenhan Yuan, Qianqian Xie, Sophia Ananiadou

The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper.

Temporal Relation Extraction

ChatGPT as a Factual Inconsistency Evaluator for Text Summarization

no code implementations27 Mar 2023 Zheheng Luo, Qianqian Xie, Sophia Ananiadou

In this paper, we particularly explore ChatGPT's ability to evaluate factual inconsistency under a zero-shot setting by examining it on both coarse-grained and fine-grained evaluation tasks including binary entailment inference, summary ranking, and consistency rating.

Abstractive Text Summarization Natural Language Inference +3

Span-based Named Entity Recognition by Generating and Compressing Information

1 code implementation10 Feb 2023 Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou

For one type of IB model, we incorporate two unsupervised generative components, span reconstruction and synonym generation, into a span-based NER system.

named-entity-recognition Named Entity Recognition +1

Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

1 code implementation7 Feb 2023 Kailai Yang, Tianlin Zhang, Hassan Alhuzali, Sophia Ananiadou

To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes.

Contrastive Learning Emotion Recognition

CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization

no code implementations26 Jan 2023 Zheheng Luo, Qianqian Xie, Sophia Ananiadou

To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on citation graphs, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references.

Contrastive Learning Text Summarization

Readability Controllable Biomedical Document Summarization

no code implementations10 Oct 2022 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 biomedical documents and the variance of readers' domain knowledge.

Document Summarization Extractive Summarization +1

GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization

no code implementations COLING 2022 Qianqian Xie, Jimin Huang, Tulika Saha, Sophia Ananiadou

Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization.

Contrastive Learning Extractive Summarization +3

Transformer for Graphs: An Overview from Architecture Perspective

1 code implementation17 Feb 2022 Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, Yu Rong

In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

no code implementations25 Jan 2022 Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, Sophia Ananiadou

Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.

Click-Through Rate Prediction Representation Learning

Investigating Text Simplification Evaluation

1 code implementation Findings (ACL) 2021 Laura Vásquez-Rodríguez, Matthew Shardlow, Piotr Przybyła, Sophia Ananiadou

Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models.

Text Simplification

One-shot to Weakly-Supervised Relation Classification using Language Models

1 code implementation AKBC 2021 Thy Thy Tran, Phong Le, Sophia Ananiadou

Unfortunately, both annotation methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation.

Relation Classification

EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature

no code implementations11 Jun 2021 Jiarun Cao, Elke M van Veen, Niels Peek, Andrew G Renehan, Sophia Ananiadou

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.

Data Augmentation named-entity-recognition +2

Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors

no code implementations NAACL 2021 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 Knowledge Base pairs.

Multi-Task Learning Relation Extraction

Paladin: an annotation tool based on active and proactive learning

no code implementations EACL 2021 Minh-Quoc Nghiem, Paul Baylis, Sophia Ananiadou

In this paper, we present Paladin, an open-source web-based annotation tool for creating high-quality multi-label document-level datasets.

Active Learning

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.

SpanEmo: Casting Multi-label Emotion Classification as Span-prediction

1 code implementation EACL 2021 Hassan Alhuzali, Sophia Ananiadou

We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence.

Classification Emotion Classification +2

DeepEventMine: end-to-end neural nested event extraction from biomedical texts

1 code implementation17 Jun 2020 Hai-Long Trieu, Thy Thy Tran, Khoa N A Duong, Anh Nguyen, Makoto Miwa, Sophia Ananiadou

Motivation Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events.

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

Revisiting Unsupervised Relation Extraction

1 code implementation ACL 2020 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).

Inductive Bias Relation Extraction

A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection

no code implementations IJCNLP 2019 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 problem on a relation graph of trigger-argument structures.

Dependency Parsing Event Detection +2

Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network

no code implementations ACL 2019 Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou

Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.

Relation Extraction

APLenty: annotation tool for creating high-quality datasets using active and proactive learning

no code implementations EMNLP 2018 Minh-Quoc Nghiem, Sophia Ananiadou

In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning.

Active Learning Multi-Label Learning +1

A Neural Layered Model for Nested Named Entity Recognition

1 code implementation NAACL 2018 Meizhi Ju, Makoto Miwa, Sophia Ananiadou

Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer.

Entity Linking named-entity-recognition +5

Paths for uncertainty: Exploring the intricacies of uncertainty identification for news

no code implementations WS 2018 Chrysoula Zerva, Sophia Ananiadou

We compare the differences in the definition and expression of uncertainty between a scientific domain, i. e., biomedicine, and newswire.

Proactive Learning for Named Entity Recognition

no code implementations WS 2017 Maolin Li, Nhung Nguyen, Sophia Ananiadou

The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i. e., they always choose the correct labels.

Active Learning named-entity-recognition +4

Distributed Document and Phrase Co-embeddings for Descriptive Clustering

no code implementations EACL 2017 Motoki Sato, Austin J. Brockmeier, Georgios Kontonatsios, Tingting Mu, John Y. Goulermas, Jun{'}ichi Tsujii, Sophia Ananiadou

Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster.

Clustering Descriptive +2

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.

Ensemble Classification of Grants using LDA-based Features

no code implementations LREC 2016 Yannis Korkontzelos, Beverley Thomas, Makoto Miwa, Sophia Ananiadou

Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making.

Classification Decision Making +1

Locating Requests among Open Source Software Communication Messages

no code implementations LREC 2014 Ioannis Korkontzelos, Sophia Ananiadou

As a first step towards assessing the quality of support offered online for Open Source Software (OSS), we address the task of locating requests, i. e., messages that raise an issue to be addressed by the OSS community, as opposed to any other message.

BIG-bench Machine Learning General Classification

Interoperability and Customisation of Annotation Schemata in Argo

no code implementations LREC 2014 Rafal Rak, Jacob Carter, Andrew Rowley, Riza Theresa Batista-Navarro, Sophia Ananiadou

Argo aids the development of custom annotation schemata and supports their interoperability by featuring a schema editor and specialised analytics for schemata alignment.


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

Collaborative Development and Evaluation of Text-processing Workflows in a UIMA-supported Web-based Workbench

no code implementations LREC 2012 Rafal Rak, Andrew Rowley, Sophia Ananiadou

Challenges in creating comprehensive text-processing worklows include a lack of the interoperability of individual components coming from different providers and/or a requirement imposed on the end users to know programming techniques to compose such workflows.


A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic.

no code implementations LREC 2012 William Black, Rob Procter, Steven Gray, Sophia Ananiadou

The analysis of a corpus of micro-blogs on the topic of the 2011 UK referendum about the Alternative Vote has been undertaken as a joint activity by text miners and social scientists.

Named Entity Recognition (NER) Sentiment Analysis +1

Cannot find the paper you are looking for? You can Submit a new open access paper.