@inproceedings{henderson-2000-neural,
    title = "A Neural Network Parser that Handles Sparse Data",
    author = "Henderson, James",
    editor = "Lavelli, Alberto  and
      Carroll, John  and
      Berwick, Robert C.  and
      Bunt, Harry C.  and
      Carpenter, Bob  and
      Carroll, John  and
      Church, Ken  and
      Johnson, Mark  and
      Joshi, Aravind  and
      Kaplan, Ronald  and
      Kay, Martin  and
      Lang, Bernard  and
      Lavie, Alon  and
      Nijholt, Anton  and
      Samuelsson, Christer  and
      Steedman, Mark  and
      Stock, Oliviero  and
      Tanaka, Hozumi  and
      Tomita, Masaru  and
      Uszkoreit, Hans  and
      Vijay-Shanker, K.  and
      Weir, David  and
      Wiren, Mats",
    booktitle = "Proceedings of the Sixth International Workshop on Parsing Technologies",
    month = feb # " 23-25",
    year = "2000",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2000.iwpt-1.14/",
    pages = "123--134",
    abstract = "Previous work has demonstrated the viability of a particular neural network architecture, Simple Synchrony Networks, for syntactic parsing. Here we present additional results on the performance of this type of parser, including direct comparisons on the same dataset with a standard statistical parsing method, Probabilistic Context Free Grammars. We focus these experiments on demonstrating one of the main advantages of the SSN parser over the PCFG, handling sparse data. We use smaller datasets than are typically used with statistical methods, resulting in the PCFG finding parses for under half of the test sentences, while the SSN finds parses for all sentences. Even on the PCFG `s parsed half, the SSN performs better than the PCFG, as measure by recall and precision on both constituents and a dependency-like measure."
}