@inproceedings{kober-etal-2017-improving,
    title = "Improving Semantic Composition with Offset Inference",
    author = "Kober, Thomas  and
      Weeds, Julie  and
      Reffin, Jeremy  and
      Weir, David",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P17-2069/",
    doi = "10.18653/v1/P17-2069",
    pages = "433--440",
    abstract = "Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition."
}