1 code implementation • WMT (EMNLP) 2020 • Rachel Bawden, Giorgio Maria Di Nunzio, Cristian Grozea, Inigo Jauregi Unanue, Antonio Jimeno Yepes, Nancy Mah, David Martinez, Aurélie Névéol, Mariana Neves, Maite Oronoz, Olatz Perez-de-Viñaspre, Massimo Piccardi, Roland Roller, Amy Siu, Philippe Thomas, Federica Vezzani, Maika Vicente Navarro, Dina Wiemann, Lana Yeganova
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities.
no code implementations • ALTA 2021 • Antonio Jimeno Yepes, Ameer Albahem, Karin Verspoor
In developing systems to identify focus entities in scientific literature, we face the problem of discriminating key entities of interest from other potentially relevant entities of the same type mentioned in the articles.
no code implementations • SMM4H (COLING) 2022 • Antonio Jimeno Yepes, Karin Verspoor
We describe the work of the READ-BioMed team for the preparation of a submission to the SocialDisNER Disease Named Entity Recognition (NER) Task (Task 10) in 2022.
no code implementations • WMT (EMNLP) 2021 • Lana Yeganova, Dina Wiemann, Mariana Neves, Federica Vezzani, Amy Siu, Inigo Jauregi Unanue, Maite Oronoz, Nancy Mah, Aurélie Névéol, David Martinez, Rachel Bawden, Giorgio Maria Di Nunzio, Roland Roller, Philippe Thomas, Cristian Grozea, Olatz Perez-de-Viñaspre, Maika Vicente Navarro, Antonio Jimeno Yepes
In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque.
no code implementations • 5 Feb 2024 • Antonio Jimeno Yepes, Yao You, Jan Milczek, Sebastian Laverde, Renyu Li
We introduce a novel framework that evaluates how chunking based on element types annotated by document understanding models contributes to the overall context and accuracy of the information retrieved.
no code implementations • 24 Aug 2023 • Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes, Jun Shen, Jiang Bian
In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction.
1 code implementation • 19 Aug 2023 • Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes
Existing approaches focus on exploiting the variable correlations in patient medical records to impute missing values and establishing time-decay mechanisms to deal with such irregularity.
no code implementations • 11 Nov 2022 • Yuxi Liu, Shaowen Qin, Antonio Jimeno Yepes, Wei Shao, Zhenhao Zhang, Flora D. Salim
Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.
no code implementations • 13 Jul 2022 • Yuxi Liu, Zhenhao Zhang, Antonio Jimeno Yepes, Flora D. Salim
Building models for health prediction based on Electronic Health Records (EHR) has become an active research area.
no code implementations • 20 Apr 2022 • Qingyu Chen, Alexis Allot, Robert Leaman, Rezarta Islamaj Doğan, Jingcheng Du, Li Fang, Kai Wang, Shuo Xu, Yuefu Zhang, Parsa Bagherzadeh, Sabine Bergler, Aakash Bhatnagar, Nidhir Bhavsar, Yung-Chun Chang, Sheng-Jie Lin, Wentai Tang, Hongtong Zhang, Ilija Tavchioski, Senja Pollak, Shubo Tian, Jinfeng Zhang, Yulia Otmakhova, Antonio Jimeno Yepes, Hang Dong, Honghan Wu, Richard Dufour, Yanis Labrak, Niladri Chatterjee, Kushagri Tandon, Fréjus Laleye, Loïc Rakotoson, Emmanuele Chersoni, Jinghang Gu, Annemarie Friedrich, Subhash Chandra Pujari, Mariia Chizhikova, Naveen Sivadasan, Zhiyong Lu
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
1 code implementation • 15 Jan 2022 • Antonio Jimeno Yepes
Deep learning methods minimise the empirical risk using loss functions such as the cross entropy loss.
3 code implementations • 8 Jun 2021 • Antonio Jimeno Yepes, Xu Zhong, Douglas Burdick
Scientific literature contain important information related to cutting-edge innovations in diverse domains.
no code implementations • 25 May 2021 • Simon Šuster, Karin Verspoor, Timothy Baldwin, Jey Han Lau, Antonio Jimeno Yepes, David Martinez, Yulia Otmakhova
The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature.
no code implementations • NAACL 2021 • Ying Xu, Xu Zhong, Antonio Jimeno Yepes, Jey Han Lau
We introduce a grey-box adversarial attack and defence framework for sentiment classification.
1 code implementation • 19 Jan 2021 • David Martinez Iraola, Antonio Jimeno Yepes
The focus of this paper is to address the knowledge acquisition bottleneck for Named Entity Recognition (NER) of mutations, by analysing different approaches to build manually-annotated data.
no code implementations • 17 Jan 2021 • Stefan Maetschke, David Martinez Iraola, Pieter Barnard, Elaheh ShafieiBavani, Peter Zhong, Ying Xu, Antonio Jimeno Yepes
A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities.
no code implementations • 18 Aug 2020 • Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes, David Martinez
We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.
no code implementations • LREC 2020 • Aur{\'e}lie N{\'e}v{\'e}ol, Antonio Jimeno Yepes, Mariana Neves
Conclusion: The information collected in this study will be used to inform test set design for the next WMT biomedical task.
6 code implementations • ECCV 2020 • Xu Zhong, Elaheh ShafieiBavani, Antonio Jimeno Yepes
In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric.
Ranked #11 on Table Recognition on PubTabNet
no code implementations • WS 2020 • Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong, David Martinez Iraola
Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining.
6 code implementations • 16 Aug 2019 • Xu Zhong, Jianbin Tang, Antonio Jimeno Yepes
Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images.
Ranked #11 on Document Layout Analysis on PubLayNet val
no code implementations • WS 2019 • Rachel Bawden, Kevin Bretonnel Cohen, Cristian Grozea, Antonio Jimeno Yepes, Madeleine Kittner, Martin Krallinger, Nancy Mah, Aurelie Neveol, Mariana Neves, Felipe Soares, Amy Siu, Karin Verspoor, Maika Vicente Navarro
In the fourth edition of the WMT Biomedical Translation task, we considered a total of six languages, namely Chinese (zh), English (en), French (fr), German (de), Portuguese (pt), and Spanish (es).
no code implementations • WS 2018 • Mariana Neves, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Cristian Grozea, Amy Siu, Madeleine Kittner, Karin Verspoor
Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e. g. research results presented in scientific publications.
no code implementations • EMNLP 2018 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
no code implementations • 13 Aug 2018 • Antonio Jimeno Yepes
Named entity recognition (NER) is used to identify relevant entities in text.
no code implementations • WS 2017 • Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Karin Verspoor, Ond{\v{r}}ej Bojar, Arthur Boyer, Cristian Grozea, Barry Haddow, Madeleine Kittner, Yvonne Lichtblau, Pavel Pecina, Rol Roller, , Rudolf Rosa, Amy Siu, Philippe Thomas, Saskia Trescher
no code implementations • IJCNLP 2017 • Quan Tran, Andrew MacKinlay, Antonio Jimeno Yepes
Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER).
no code implementations • 19 May 2017 • Antonio Jimeno Yepes, Jianbin Tang, Benjamin Scott Mashford
We achieve this by training directly a binary hardware crossbar that accommodates the TrueNorth axon configuration constrains and we propose a different neuron model.
no code implementations • 26 Oct 2016 • A. K. M. Sabbir, Antonio Jimeno Yepes, Ramakanth Kavuluru
In this paper, we employ knowledge-based approaches that also exploit recent advances in neural word/concept embeddings to improve over the state-of-the-art in biomedical WSD using the MSH WSD dataset as the test set.
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Liane Guillou, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Pavel Pecina, Martin Popel, Philipp Koehn, Christof Monz, Matteo Negri, Matt Post, Lucia Specia, Karin Verspoor, J{\"o}rg Tiedemann, Marco Turchi
no code implementations • WS 2016 • Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aur{\'e}lie N{\'e}v{\'e}ol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri
no code implementations • 25 May 2016 • Antonio Jimeno Yepes, Jianbin Tang
Previous work has achieved this by training a network to learn continuous probabilities and deployment to a neuromorphic architecture by random sampling these probabilities.
no code implementations • LREC 2016 • Mariana Neves, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol
We show that for all language pairs, a statistical machine translation system trained on the parallel corpora achieves performance that rivals or exceeds the state of the art in the biomedical domain.
no code implementations • 9 Apr 2016 • Antonio Jimeno Yepes
Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word.