The journal Communication Methods and Measures has recently published a paper titled "Automatically Finding Actors in Texts: A Performance Review of Multilingual Named Entity Recognition Tools" authored by Paul Balluff, Hajo Boomgaarden, and Annie Waldherr. The study assesses and compares various multilingual Named Entity Recognition (NER) tools. 

Named Entity Recognition (NER) is a crucial task in natural language processing and has a wide range of applications in communication science. However, there is a lack of systematic evaluations of available NER tools in the field. In this study, the authors evaluate the performance of various multilingual NER tools, including rule-based and transformer-based models. They conducted experiments on corpora containing texts in multiple languages and evaluated the F1-score, speed, and features of each tool. 

The results show that transformer-based language models outperform rule-based models and other NER tools in most languages. However, the performance of the transformer-based models varies depending on the language and the corpus. The study provides insights into the strengths and weaknesses of NER tools and their suitability for specific languages, which can inform the selection of appropriate tools for future studies and applications in communication science. 

For those interested in exploring the full details of this research, the open-access paper can be accessed at: https://www.tandfonline.com/doi/full/10.1080/19312458.2024.2324789 

Cite article: 

Balluff, P., Boomgaarden, H. G., & Waldherr, A. (2024). Automatically Finding Actors in Texts: A Performance Review of Multilingual Named Entity Recognition Tools. Communication Methods and Measures. DOI: 10.1080/19312458.2024.2324789