@inproceedings{koper-schulte-im-walde-2018-analogies,
    title = "Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/N18-2024/",
    doi = "10.18653/v1/N18-2024",
    pages = "150--156",
    abstract = "We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to corpus-based studies, a novel dataset demonstrates that ``regular'' shifts represent the smallest class. Classification experiments relying on a standard similarity model successfully distinguish between four types of shifts, with verb classes boosting the performance, and affective features for abstractness, emotion and sentiment representing the most salient indicators."
}