Kalina Bontcheva

Other people with similar names: Katina Bontcheva


2021

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European Language Grid : A Joint Platform for the European Language Technology CommunityEuropean Language Grid: A Joint Platform for the European Language Technology Community
Georg Rehm | Stelios Piperidis | Kalina Bontcheva | Jan Hajic | Victoria Arranz | Andrejs Vasiļjevs | Gerhard Backfried | Jose Manuel Gomez-Perez | Ulrich Germann | Rémi Calizzano | Nils Feldhus | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Julian Moreno-Schneider | Dimitris Galanis | Penny Labropoulou | Miltos Deligiannis | Katerina Gkirtzou | Athanasia Kolovou | Dimitris Gkoumas | Leon Voukoutis | Ian Roberts | Jana Hamrlova | Dusan Varis | Lukas Kacena | Khalid Choukri | Valérie Mapelli | Mickaël Rigault | Julija Melnika | Miro Janosik | Katja Prinz | Andres Garcia-Silva | Cristian Berrio | Ondrej Klejch | Steve Renals
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.

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Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials
Greg Kondrak | Kalina Bontcheva | Dan Gillick
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials

2020

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Measuring What Counts : The Case of Rumour Stance Classification
Carolina Scarton | Diego Silva | Kalina Bontcheva
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies towards a given rumour, namely support, deny, question, or comment. Numerous methods have been proposed and their performance compared in the RumourEval shared tasks in 2017 and 2019. Results demonstrated that this is a challenging problem since naturally occurring rumour stance data is highly imbalanced. This paper specifically questions the evaluation metrics used in these shared tasks. We re-evaluate the systems submitted to the two RumourEval tasks and show that the two widely adopted metrics accuracy and macro-F1 are not robust for the four-class imbalanced task of rumour stance classification, as they wrongly favour systems with highly skewed accuracy towards the majority class. To overcome this problem, we propose new evaluation metrics for rumour stance detection. These are not only robust to imbalanced data but also score higher systems that are capable of recognising the two most informative minority classes (support and deny).

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Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour
Xingyi Song | Johnny Downs | Sumithra Velupillai | Rachel Holden | Maxim Kikoler | Kalina Bontcheva | Rina Dutta | Angus Roberts
Proceedings of the 12th Language Resources and Evaluation Conference

Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.

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The European Language Technology Landscape in 2020 : Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual EuropeEuropean Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm | Katrin Marheinecke | Stefanie Hegele | Stelios Piperidis | Kalina Bontcheva | Jan Hajič | Khalid Choukri | Andrejs Vasiļjevs | Gerhard Backfried | Christoph Prinz | José Manuel Gómez-Pérez | Luc Meertens | Paul Lukowicz | Josef van Genabith | Andrea Lösch | Philipp Slusallek | Morten Irgens | Patrick Gatellier | Joachim Köhler | Laure Le Bars | Dimitra Anastasiou | Albina Auksoriūtė | Núria Bel | António Branco | Gerhard Budin | Walter Daelemans | Koenraad De Smedt | Radovan Garabík | Maria Gavriilidou | Dagmar Gromann | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Jan Odijk | Maciej Ogrodniczuk | Eiríkur Rögnvaldsson | Mike Rosner | Bolette Pedersen | Inguna Skadiņa | Marko Tadić | Dan Tufiș | Tamás Váradi | Kadri Vider | Andy Way | François Yvon
Proceedings of the 12th Language Resources and Evaluation Conference

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI including many opportunities, synergies but also misconceptions has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

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Proceedings of the 1st International Workshop on Language Technology Platforms
Georg Rehm | Kalina Bontcheva | Khalid Choukri | Jan Hajič | Stelios Piperidis | Andrejs Vasiļjevs
Proceedings of the 1st International Workshop on Language Technology Platforms

2019

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Journalist-in-the-Loop : Continuous Learning as a Service for Rumour Analysis
Twin Karmakharm | Nikolaos Aletras | Kalina Bontcheva
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.

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SemEval-2019 Task 7 : RumourEval, Determining Rumour Veracity and Support for RumoursSemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Genevieve Gorrell | Elena Kochkina | Maria Liakata | Ahmet Aker | Arkaitz Zubiaga | Kalina Bontcheva | Leon Derczynski
Proceedings of the 13th International Workshop on Semantic Evaluation

Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of fake news has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour’s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks ; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70 % increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.

2017

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SemEval-2017 Task 8 : RumourEval : Determining rumour veracity and support for rumoursSemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski | Kalina Bontcheva | Maria Liakata | Rob Procter | Geraldine Wong Sak Hoi | Arkaitz Zubiaga
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Media is full of false claims. Even Oxford Dictionaries named post-truth as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the nature of the discourse around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics each having their own families of claims and replies and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.
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