Ian Roberts


2020

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European Language Grid: An Overview
Georg Rehm | Maria Berger | Ela Elsholz | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Stelios Piperidis | Miltos Deligiannis | Dimitris Galanis | Katerina Gkirtzou | Penny Labropoulou | Kalina Bontcheva | David Jones | Ian Roberts | Jan Hajič | Jana Hamrlová | Lukáš Kačena | Khalid Choukri | Victoria Arranz | Andrejs Vasiļjevs | Orians Anvari | Andis Lagzdiņš | Jūlija Meļņika | Gerhard Backfried | Erinç Dikici | Miroslav Janosik | Katja Prinz | Christoph Prinz | Severin Stampler | Dorothea Thomas-Aniola | José Manuel Gómez-Pérez | Andres Garcia Silva | Christian Berrío | Ulrich Germann | Steve Renals | Ondrej Klejch
Proceedings of the 12th Language Resources and Evaluation Conference

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.

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Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
Georg Rehm | Dimitris Galanis | Penny Labropoulou | Stelios Piperidis | Martin Welß | Ricardo Usbeck | Joachim Köhler | Miltos Deligiannis | Katerina Gkirtzou | Johannes Fischer | Christian Chiarcos | Nils Feldhus | Julian Moreno-Schneider | Florian Kintzel | Elena Montiel | Víctor Rodríguez Doncel | John Philip McCrae | David Laqua | Irina Patricia Theile | Christian Dittmar | Kalina Bontcheva | Ian Roberts | Andrejs Vasiļjevs | Andis Lagzdiņš
Proceedings of the 1st International Workshop on Language Technology Platforms

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.

2019

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Reasoning Over Paths via Knowledge Base Completion
Saatviga Sudhahar | Andrea Pierleoni | Ian Roberts
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.

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Deep Bidirectional Transformers for Relation Extraction without Supervision
Yannis Papanikolaou | Ian Roberts | Andrea Pierleoni
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base. Our approach leverages syntactic parsing and pre-trained word embeddings to extract few but precise relations, which are then used to annotate a larger corpus, in a manner identical to distant supervision. The resulting data set is employed to fine tune a pre-trained BERT model in order to perform relation extraction. Empirical evaluation on four data sets from the biomedical domain shows that our method significantly outperforms two simple baselines for unsupervised relation extraction and, even if not using any supervision at all, achieves slightly worse results than the state-of-the-art in three out of four data sets. Importantly, we show that it is possible to successfully fine tune a large pretrained language model with noisy data, as opposed to previous works that rely on gold data for fine tuning.

2016

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Broad Twitter Corpus: A Diverse Named Entity Recognition Resource
Leon Derczynski | Kalina Bontcheva | Ian Roberts
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

One of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous to the CoNLL’2003 news dataset. For instance, the biggest Ritter tweet corpus is only 45,000 tokens – a mere 15% the size of CoNLL’2003. Another major shortcoming is the lack of temporal, geographic, and author diversity. This paper introduces the Broad Twitter Corpus (BTC), which is not only significantly bigger, but sampled across different regions, temporal periods, and types of Twitter users. The gold-standard named entity annotations are made by a combination of NLP experts and crowd workers, which enables us to harness crowd recall while maintaining high quality. We also measure the entity drift observed in our dataset (i.e. how entity representation varies over time), and compare to newswire. The corpus is released openly, including source text and intermediate annotations.

2014

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The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy
Kalina Bontcheva | Ian Roberts | Leon Derczynski | Samantha Alexander-Eames
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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AnnoMarket: An Open Cloud Platform for NLP
Valentin Tablan | Kalina Bontcheva | Ian Roberts | Hamish Cunningham | Marin Dimitrov
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2004

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A Large-Scale Resource for Storing and Recognizing Technical Terminology
Henk Harkema | Robert Gaizauskas | Mark Hepple | Neil Davis | Yikun Guo | Angus Roberts | Ian Roberts
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A Large Scale Terminology Resource for Biomedical Text Processing
Henk Harkema | Robert Gaizauskas | Mark Hepple | Angus Roberts | Ian Roberts | Neil Davis | Yikun Guo
HLT-NAACL 2004 Workshop: Linking Biological Literature, Ontologies and Databases