@inproceedings{bott-schulte-im-walde-2017-factoring,
    title = "Factoring Ambiguity out of the Prediction of Compositionality for {G}erman Multi-Word Expressions",
    author = "Bott, Stefan  and
      Schulte im Walde, Sabine",
    editor = "Markantonatou, Stella  and
      Ramisch, Carlos  and
      Savary, Agata  and
      Vincze, Veronika",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-1708/",
    doi = "10.18653/v1/W17-1708",
    pages = "66--72",
    abstract = {Ambiguity represents an obstacle for distributional semantic models(DSMs), which typically subsume the contexts of all word senses within one vector. While individual vector space approaches have been concerned with sense discrimination (e.g., Sch{\"u}tze 1998, Erk 2009, Erk and Pado 2010), such discrimination has rarely been integrated into DSMs across semantic tasks. This paper presents a soft-clustering approach to sense discrimination that filters sense-irrelevant features when predicting the degrees of compositionality for German noun-noun compounds and German particle verbs.}
}