@inproceedings{mahdizadeh-sani-etal-2024-diachronic,
    title = "What Can Diachronic Contexts and Topics Tell Us about the Present-Day Compositionality of {E}nglish Noun Compounds?",
    author = "Mahdizadeh Sani, Samin  and
      Rassem, Malak  and
      Jenkins, Chris  and
      Mileti{\'c}, Filip  and
      Schulte im Walde, Sabine",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1517",
    pages = "17449--17458",
    abstract = "Predicting the compositionality of noun compounds such as climate change and tennis elbow is a vital component in natural language understanding. While most previous computational methods that automatically determine the semantic relatedness between compounds and their constituents have applied a synchronic perspective, the current study investigates what diachronic changes in contexts and semantic topics of compounds and constituents reveal about the compounds{'} present-day degrees of compositionality. We define a binary classification task that utilizes two diachronic vector spaces based on contextual co-occurrences and semantic topics, and demonstrate that diachronic changes in cosine similarities {--} measured over context or topic distributions {--} uncover patterns that distinguish between compounds with low and high present-day compositionality. Despite fewer dimensions in the topic models, the topic space performs on par with the co-occurrence space and captures rather similar information. Temporal similarities between compounds and modifiers as well as between compounds and their prepositional paraphrases predict the compounds{'} present-day compositionality with accuracy {\textgreater}0.7.",
}

Predicting the compositionality of noun compounds such as climate change and tennis elbow is a vital component in natural language understanding. While most previous computational methods that automatically determine the semantic relatedness between compounds and their constituents have applied a synchronic perspective, the current study investigates what diachronic changes in contexts and semantic topics of compounds and constituents reveal about the compounds{'} present-day degrees of compositionality. We define a binary classification task that utilizes two diachronic vector spaces based on contextual co-occurrences and semantic topics, and demonstrate that diachronic changes in cosine similarities {--} measured over context or topic distributions {--} uncover patterns that distinguish between compounds with low and high present-day compositionality. Despite fewer dimensions in the topic models, the topic space performs on par with the co-occurrence space and captures rather similar information. Temporal similarities between compounds and modifiers as well as between compounds and their prepositional paraphrases predict the compounds{'} present-day compositionality with accuracy {\textgreater}0.7.
Mahdizadeh Sani, Samin Rassem, Malak Jenkins, Chris Mileti{\'c}, Filip Schulte im Walde, Sabine
What Can Diachronic Contexts and Topics Tell Us about the Present-Day Compositionality of English Noun Compounds?