Abstract
This paper presents a toolkit that applies named-entity extraction techniques to identify information related to criminal activity in texts from the Polish Internet. The methodological and technical assumptions were established following the requirements of our application users from the Border Guard. Due to the specificity of the users’ needs and the specificity of web texts, we used original methodologies related to the search for desired texts, the creation of domain lexicons, the annotation of the collected text resources, and the combination of rule-based and machine-learning techniques for extracting the information desired by the user. The performance of our tools has been evaluated on 6240 manually annotated text fragments collected from Internet sources. Evaluation results and user feedback show that our approach is feasible and has potential value for real-life applications in the daily work of border guards. Lexical lookup combined with hand-crafted rules and regular expressions, supported by text statistics, can make a decent specialized entity recognition system in the absence of large data sets required for training a good neural network.- Anthology ID:
- 2022.lrec-1.669
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6223–6231
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.669
- DOI:
- Cite (ACL):
- Paweł Skórzewski, Mikołaj Pieniowski, and Grazyna Demenko. 2022. Named Entity Recognition to Detect Criminal Texts on the Web. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6223–6231, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Named Entity Recognition to Detect Criminal Texts on the Web (Skórzewski et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.669.pdf