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 • BioNLP (ACL) 2022 • Hai-Long Trieu, Makoto Miwa, Sophia Ananiadou
Cancer immunology research involves several important cell and protein factors.
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
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
Click-Through Rate (CTR) prediction, is an essential component of online advertising.
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 #8 on
Named Entity Recognition
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, 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 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 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 • 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 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 • 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 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 • 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 • 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.
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.