Manolis Kyriakakis


2025

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GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek
Lefteris Loukas | Nikolaos Smyrnioudis | Chrysa Dikonomaki | Spiros Barbakos | Anastasios Toumazatos | John Koutsikakis | Manolis Kyriakakis | Mary Georgiou | Stavros Vassos | John Pavlopoulos | Ion Androutsopoulos
Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations

We present GR-NLP-TOOLKIT, an open-source natural language processing (NLP) toolkit developed specifically for modern Greek. The toolkit provides state-of-the-art performance in five core NLP tasks, namely part-of-speech tagging, morphological tagging, dependency parsing, named entity recognition, and Greeklish-to-Greek transliteration. The toolkit is based on pre-trained Transformers, it is freely available, and can be easily installed in Python (pip install gr-nlp-toolkit). It is also accessible through a demonstration platform on HuggingFace, along with a publicly available API for non-commercial use. We discuss the functionality provided for each task, the underlying methods, experiments against comparable open-source toolkits, and future possible enhancements. The toolkit is available at: https://github.com/nlpaueb/gr-nlp-toolkit

2019

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Transfer Learning for Causal Sentence Detection
Manolis Kyriakakis | Ion Androutsopoulos | Artur Saudabayev | Joan Ginés i Ametllé
Proceedings of the 18th BioNLP Workshop and Shared Task

We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts. To bypass the scarcity of causal instances in relation extraction datasets, we exploit transfer learning, namely ELMO and BERT, using a bidirectional GRU with self-attention ( BIGRUATT ) as a baseline. We experiment with both generic public relation extraction datasets and a new biomedical causal sentence detection dataset, a subset of which we make publicly available. We find that transfer learning helps only in very small datasets. With larger datasets, BIGRUATT reaches a performance plateau, then larger datasets and transfer learning do not help.