@inproceedings{goldstein-berger-2026-temporal,
    title = "Temporal Expression Recognition in Legal Transcripts",
    author = "Goldstein, Elizabeth J.  and
      Berger, Maria",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.lrec-1.478/",
    doi = "10.63317/5n7bd6gxobss",
    pages = "6022--6037",
    abstract = "In litigation, trial transcripts provide verbatim records of witness testimony, primarily given in response to attorney questioning. To effectively analyze these transcripts, lawyers must often reconstruct events in chronological order{---}a task that begins with identifying dates associated with testified facts. This paper introduces two datasets for temporal expression extraction from legal transcripts: a primary dataset derived from a lengthy 1995 U.S. criminal trial, and a smaller robustness-testing dataset drawn from seven other legal proceedings. We evaluate semi-supervised approaches for date entity recognition, fine-tuning neural models on weakly labeled training data, and benchmarking them against both small and large language models. Our best-performing models achieve 83{\%} F1-score on the primary dataset (FLAIR rule-modified) and 72{\%} F1-score on the cross-domain, small test set (BERT-cased). These results, alongside our annotated datasets and corresponding experiments, provide a foundation for developing robust date extraction and temporal ordering tools for speech-derived legal text. Moreover, we identify unique challenges for state-of-the-art NER models on legal transcripts, including legal terminology and multiple anchor date resolution."
}