@inproceedings{gagnier-kirubakaran-2026-benchmark,
    title = "A Benchmark and Evaluation of Automated Language of Study Extraction from Computational Linguistics Publications",
    author = "Gagnier, Henry  and
      Kirubakaran, Ashwin",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.26/",
    pages = "366--374",
    ISBN = "979-8-89176-383-8",
    abstract = "Language of study is an aspect of computational linguistics papers that is useful for analyses of trends and diversity in computational linguistics. This study introduces the first benchmark and evaluation of automated language of study extraction from computational linguistics publications. The benchmark containing 431 publications from the ACL Anthology, with 62 languages analyzed, was annotated. SciBERT and four large language models (LLMs), GPT-4o mini, Gemini 2.5 Flash, Claude 3.5 Haiku, and DeepSeek 3.2, were evaluated on the benchmark using different parts of the ACL Anthology papers. GPT-4o mini achieved the best exact match and Jaccard agreement scores of 0.646 and 0.687, respectively, which is slightly less than the agreement in human annotation. Gemini 2.5 Flash achieved the best micro F1 of 0.633. Models using the abstract for extraction were competitive with models using the full text, showing that accuracy can be achieved in language of study extraction without high computational costs. These findings demonstrate that LLMs are able to accurately identify the languages of study in computational linguistics papers, potentially reducing the time and cost of analyses in computational linguistics."
}