AUEB Natural Language Processing Group


AUEB's Natural Language Processing Group develops algorithms, models, and systems that allow computers to process and generate natural language texts.

The group's current research interests include:
  • question answering, especially for biomedical document collections,
  • natural language generation from structured data, and more recently from medical images,
  • text classification, including filtering abusive content,
  • information extraction and opinion mining, including legal text analytics and sentiment analysis,
  • natural language processing tools for Greek,
  • machine learning in natural language processing, especially deep learning,
  • natural language processing in digital curation.

The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business.

Members of the group co-authored the paper "Restoring and attributing ancient texts using deep neural networks", which was published in Nature (March 2022). The group co-organized the 3rd Workshop on Natural Legal Language Processing (NLLP 2021) at EMNLP 2021, the SemEval Toxic Spans Detection task (2021), the 11th EETN (Greek) Conference on Artificial Intelligence (SETN 2020), the 2nd Workshop on Natural Legal Language Processing (NLLP 2020) at KDD 2020, the 1st Athens Natural Language Processing Summer School (AthNLP 2019), the EACL 2009 conference in Athens, the Large Scale Hierarchical Text Classification challenges (LSHTC3 was the ECML/PKDD 2012 Discovery Challenge), the BioASQ challenges, and the SemEval Aspect-Based Sentiment Analysis task (2014, 2015, 2016).

The group ranked 1st in concept detection and 3rd in caption prediction in ImageCLEFmed Caption 2023 (see also this AUEB announcement in Greek). We also ranked 1st in concept detection and 2nd in caption prediction in ImageCLEFmed Caption 2021 and ImageCLEFmed Caption 2022. Our systems were also ranked at positions 1, 2, 3 and 5 among approximately 60 systems in the ImageCLEFmed Caption 2019 task, and at positions 1, 2, 6 among 49 systems in ImageCLEFmed Caption 2020 (see also this AUEB announcement). The group received a BioASQ award in 2018 for ranking first in three out of five document retrieval test batches and all five snippet retrieval test batches. We received another BioASQ award in 2019 for ranking first in the four document and snippet retrieval batches we participated in. We also received a BioASQ award in 2020 for ranking in the top 2 positions in 4 out of 5 document retrieval test batches, and for ranking 1st in 4 out of 5 snippet retrieval test batches.