@inproceedings{yu-etal-2024-ad,
    title = "Ad Hoc Compounds for Stance Detection",
    author = "Yu, Qi  and
      Schlotterbeck, Fabian  and
      Wang, Hening  and
      Reichmann, Naomi  and
      Stolterfoht, Britta  and
      Eckardt, Regine  and
      Butt, Miriam",
    editor = {Bhatia, Archna  and
      Bouma, Gosse  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Evang, Kilian  and
      Garcia, Marcos  and
      Giouli, Voula  and
      Han, Lifeng  and
      Nivre, Joakim  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.mwe-1.27",
    pages = "231--242",
    abstract = "In this paper we focus on a subclass of multi-word expressions, namely compound formation in German. The automatic detection of compounds is a known problem and we argue that its resolution should be given more urgency in light of a new role we uncovered with respect to ad hoc compound formation: the systematic expression of attitudinal meaning and its potential importance for the down-stream NLP task of stance detection. We demonstrate that ad hoc compounds in German indeed systematically express attitudinal meaning by adducing corpus linguistic and psycholinguistic experimental data. However, an investigation of state-of-the-art dependency parsers and Universal Dependency treebanks shows that German compounds are parsed and annotated very unevenly, so that currently one cannot reliably identify or access ad hoc compounds with attitudinal meaning in texts. Moreover, we report initial experiments with large language models underlining the challenges in capturing attitudinal meanings conveyed by ad hoc compounds. We consequently suggest a systematized way of annotating (and thereby also parsing) ad hoc compounds that is based on positive experiences from within the multilingual ParGram grammar development effort.",
}

In this paper we focus on a subclass of multi-word expressions, namely compound formation in German. The automatic detection of compounds is a known problem and we argue that its resolution should be given more urgency in light of a new role we uncovered with respect to ad hoc compound formation: the systematic expression of attitudinal meaning and its potential importance for the down-stream NLP task of stance detection. We demonstrate that ad hoc compounds in German indeed systematically express attitudinal meaning by adducing corpus linguistic and psycholinguistic experimental data. However, an investigation of state-of-the-art dependency parsers and Universal Dependency treebanks shows that German compounds are parsed and annotated very unevenly, so that currently one cannot reliably identify or access ad hoc compounds with attitudinal meaning in texts. Moreover, we report initial experiments with large language models underlining the challenges in capturing attitudinal meanings conveyed by ad hoc compounds. We consequently suggest a systematized way of annotating (and thereby also parsing) ad hoc compounds that is based on positive experiences from within the multilingual ParGram grammar development effort.
Yu, Qi Schlotterbeck, Fabian Wang, Hening Reichmann, Naomi Stolterfoht, Britta Eckardt, Regine Butt, Miriam
Ad Hoc Compounds for Stance Detection