@inproceedings{bogdanova-bucur-2026-pmi,
    title = "{PMI} {MWE} Scorer at {PARSEME} 2.0 Subtask 1: identifying multi-word expressions using pointwise mutual information and universal dependencies",
    author = "Bogdanova, Anna  and
      Bucur, Ileana",
    editor = {Ojha, Atul Kr.  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Stoyanova, Ivelina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.mwe-1.22/",
    pages = "165--169",
    ISBN = "979-8-89176-363-0",
    abstract = "Multi-word expressions (MWEs) remain a challenge for NLP systems due to their syntactic variability and non-compositional semantics, that is why this issue was proposed as shared task within Unidive organization. With increasing popularity of large language models (LLM) it is important to continue researching alternative solutions. One of classical approaches for identifying MWEs is calculating pointwise mutual information (PMI), but this is a purely statistical approach that cannot unveil the links between words in natural text. To fix this issue we propose this paper with a simple syntax-aware PMI method that leverages Universal Dependency (UD) trees (Nivre et al.,2016) to model co-occurrence between syntactically related words. By computing PMI over dependency-linked word pairs and aggregating these scores, we aim to improve surface-based methods. Opposed to expectations, our experiment shows that classical statistical approach gets better results in identifying MWEs partially. Still, this approach is aimed to find a balance between lightweight calculations as opposed to LLMs and precision in results."
}