no code implementations • Findings (EMNLP) 2021 • Jiarun Cao, Sophia Ananiadou
During the training stage, GenerativeRE fine-tunes the pre-trained generative model and learns the special entity labels simultaneously.
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.
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.
no code implementations • BioNLP (ACL) 2022 • Hai-Long Trieu, Makoto Miwa, Sophia Ananiadou
Cancer immunology research involves several important cell and protein factors.
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.
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.
Ranked #1 on
Text Summarization
on S2ORC
no code implementations • 19 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.
no code implementations • 1 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.
1 code implementation • 2 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.
no code implementations • 29 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.
2 code implementations • 24 Sep 2023 • Kailai Yang, Tianlin Zhang, Ziyan Kuang, Qianqian Xie, Sophia Ananiadou, Jimin Huang
The raw social media data are collected from 10 existing sources covering 8 mental health analysis tasks.
1 code implementation • 21 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.
1 code implementation • 9 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.
1 code implementation • 5 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.
1 code implementation • 23 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.
no code implementations • 20 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.
no code implementations • 19 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.
no code implementations • 18 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.
no code implementations • 11 Apr 2023 • Chenhan Yuan, Qianqian Xie, Sophia Ananiadou
The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper.
2 code implementations • 6 Apr 2023 • Kailai Yang, Shaoxiong Ji, Tianlin Zhang, Qianqian Xie, Ziyan Kuang, Sophia Ananiadou
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.
no code implementations • 27 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
1 code implementation • 10 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.
1 code implementation • 7 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.
no code implementations • 26 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.
no code implementations • 10 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.
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.
Ranked #9 on
Text Summarization
on Pubmed
1 code implementation • ACL 2022 • Jake Vasilakes, Chrysoula Zerva, Makoto Miwa, Sophia Ananiadou
Negation and uncertainty modeling are long-standing tasks in natural language processing.
1 code implementation • 17 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.
no code implementations • 25 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.
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.
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.
no code implementations • 11 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.
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.
Ranked #4 on
Relation Extraction
on NYT Corpus
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.
no code implementations • 23 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.
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.
Ranked #1 on
Emotion Classification
on SemEval 2018 Task 1E-c
no code implementations • COLING 2020 • Maolin Li, Hiroya Takamura, Sophia Ananiadou
To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels.
1 code implementation • 17 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.
Ranked #1 on
Event Extraction
on GENIA 2013
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.
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).
no code implementations • WS 2019 • Hai-Long Trieu, Anh-Khoa Duong Nguyen, Nhung Nguyen, Makoto Miwa, Hiroya Takamura, Sophia Ananiadou
Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.
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.
1 code implementation • IJCNLP 2019 • Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We thus propose an edge-oriented graph neural model for document-level relation extraction.
no code implementations • WS 2019 • Hassan Alhuzali, Sophia Ananiadou
The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs).
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.
1 code implementation • NAACL 2019 • Maolin Li, Arvid Fahlström Myrman, Tingting Mu, Sophia Ananiadou
It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance.
1 code implementation • ACL 2018 • Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
We present a novel graph-based neural network model for relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
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.
no code implementations • WS 2018 • Hai-Long Trieu, Nhung T. H. Nguyen, Makoto Miwa, Sophia Ananiadou
Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers.
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.
Ranked #10 on
Named Entity Recognition (NER)
on GENIA
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.
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.
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.
no code implementations • WS 2016 • Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, Sophia Ananiadou
Named entity recognition (NER) in social media (e. g., Twitter) is a challenging task due to the noisy nature of text.
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.
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.
no code implementations • LREC 2014 • Claudiu Mih{\u{a}}il{\u{a}}, Sophia Ananiadou
Causality lies at the heart of biomedical knowledge, being involved in diagnosis, pathology or systems biology.
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.
no code implementations • LREC 2014 • Georg Rehm, Hans Uszkoreit, Sophia Ananiadou, N{\'u}ria Bel, Audron{\.e} Bielevi{\v{c}}ien{\.e}, Lars Borin, Ant{\'o}nio Branco, Gerhard Budin, Nicoletta Calzolari, Walter Daelemans, Radovan Garab{\'\i}k, Marko Grobelnik, Carmen Garc{\'\i}a-Mateo, Josef van Genabith, Jan Haji{\v{c}}, Inma Hern{\'a}ez, John Judge, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lind{\'e}n, Bernardo Magnini, Joseph Mariani, John McNaught, Maite Melero, Monica Monachini, Asunci{\'o}n Moreno, Jan Odijk, Maciej Ogrodniczuk, Piotr P{\k{e}}zik, Stelios Piperidis, Adam Przepi{\'o}rkowski, Eir{\'\i}kur R{\"o}gnvaldsson, Michael Rosner, Bolette Pedersen, Inguna Skadi{\c{n}}a, Koenraad De Smedt, Marko Tadi{\'c}, Paul Thompson, Dan Tufi{\c{s}}, Tam{\'a}s V{\'a}radi, Andrejs Vasi{\c{l}}jevs, Kadri Vider, Jolanta Zabarskaite
This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics.
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.
no code implementations • LREC 2012 • Xinkai Wang, Paul Thompson, Jun{'}ichi Tsujii, Sophia Ananiadou
All experiments compare the use of two different retrieval models, i. e. Okapi BM25 and a query likelihood language model.
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.
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.
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.