Comparative Study of Recurrent and Dense Neural Networks for Classifying Maritime Terms

• Published in: 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA) pp. 1 - 6
• Main Authors: Mouratidis, Despoina, Kermanidis, Katia, Kanavos, Andreas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10-07-2023
• Subjects: Annotations; Deep learning; Dictionaries; Domain-Specific Terms; Gaze Detection; Industries; Law; Legal Texts; Maritime Texts; Neural networks; Tagging; Term Extraction; Text Tagging
• DOI: 10.1109/IISA59645.2023.10345925

Despite its importance, the extraction of domain-specific terms has not been sufficiently studied. This paper proposes an automated approach for semi-supervised term extraction from Greek-language legal documents related to the shipping/maritime industry. The approach employs a deep learning scheme based on a machine learning model that uses several linguistic features and word embeddings, and is trained using two different kinds of ground truth: the size of words in characters and the use of freely available nautical dictionaries. In addition, the approach was able to extract a large number of domain-specific terms that were not included in existing dictionaries. The results show that the proposed approach outperforms human annotation when a semi-supervised method is used for ground truth. These results suggest that the proposed approach could be a useful tool for term extraction from legal documents in the shipping/maritime industry, and could potentially be adapted for other domains and languages as well.