Georgios Paliouras


2022

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Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization
Georgios Katsimpras | Georgios Paliouras
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial. Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study. Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention. Then, an evidence sentence, which conveys information about the effectiveness of the intervention, is extracted automatically from each abstract. Based on the set of evidence sentences extracted from the abstracts, a short summary about the intervention is constructed. Finally, the produced summaries are used to train a BERT-based classifier, in order to infer the effectiveness of an intervention. To evaluate our proposed method, we introduce a new dataset which is a collection of clinical trials together with their associated PubMed articles. Our experiments, demonstrate the effectiveness of producing short informative summaries and using them to predict the effectiveness of an intervention.

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FiNER: Financial Numeric Entity Recognition for XBRL Tagging
Lefteris Loukas | Manos Fergadiotis | Ilias Chalkidis | Eirini Spyropoulou | Prodromos Malakasiotis | Ion Androutsopoulos | Georgios Paliouras
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Publicly traded companies are required to submit periodic reports with eXtensive Business Reporting Language (XBRL) word-level tags. Manually tagging the reports is tedious and costly. We, therefore, introduce XBRL tagging as a new entity extraction task for the financial domain and release FiNER-139, a dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction datasets, FiNER-139 uses a much larger label set of 139 entity types. Most annotated tokens are numeric, with the correct tag per token depending mostly on context, rather than the token itself. We show that subword fragmentation of numeric expressions harms BERT’s performance, allowing word-level BILSTMs to perform better. To improve BERT’s performance, we propose two simple and effective solutions that replace numeric expressions with pseudo-tokens reflecting original token shapes and numeric magnitudes. We also experiment with FIN-BERT, an existing BERT model for the financial domain, and release our own BERT (SEC-BERT), pre-trained on financial filings, which performs best. Through data and error analysis, we finally identify possible limitations to inspire future work on XBRL tagging.

2018

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Results of the sixth edition of the BioASQ Challenge
Anastasios Nentidis | Anastasia Krithara | Konstantinos Bougiatiotis | Georgios Paliouras | Ioannis Kakadiaris
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering

This paper presents the results of the sixth edition of the BioASQ challenge. The BioASQ challenge aims at the promotion of systems and methodologies through the organization of a challenge on two tasks: semantic indexing and question answering. In total, 26 teams with more than 90 systems participated in this year’s challenge. As in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the-art systems are continuously improving, pushing the frontier of research.

2017

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Results of the fifth edition of the BioASQ Challenge
Anastasios Nentidis | Konstantinos Bougiatiotis | Anastasia Krithara | Georgios Paliouras | Ioannis Kakadiaris
BioNLP 2017

The goal of the BioASQ challenge is to engage researchers into creating cuttingedge biomedical information systems. Specifically, it aims at the promotion of systems and methodologies that are able to deal with a plethora of different tasks in the biomedical domain. This is achieved through the organization of challenges. The fifth challenge consisted of three tasks: semantic indexing, question answering and a new task on information extraction. In total, 29 teams with more than 95 systems participated in the challenge. Overall, as in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the art systems are continuously improving, pushing the frontier of research.

2016

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Results of the 4th edition of BioASQ Challenge
Anastasia Krithara | Anastasios Nentidis | Georgios Paliouras | Ioannis Kakadiaris
Proceedings of the Fourth BioASQ workshop

2002

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Ellogon: A New Text Engineering Platform
Georgios Petasis | Vangelis Karkaletsis | Georgios Paliouras | Ion Androutsopoulos | Constantine D. Spyropoulos
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Using Machine Learning to Maintain Rule-based Named-Entity Recognition and Classification Systems
Georgios Petasis | Frantz Vichot | Francis Wolinski | Georgios Paliouras | Vangelis Karkaletsis | Constantine D. Spyropoulos
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

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Stacking Classifiers for Anti-Spam Filtering of E-Mail
Georgios Sakkis | Ion Androutsopoulos | Georgios Paliouras | Vangelis Karkaletsis | Constantine D. Spyropoulos | Panagiotis Stamatopoulos
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing