@inproceedings{Simpson_and_Martin_2003,
  author = "Simpson, A.~C. and Martin, A.~P.",
  booktitle = "Proceedings of the BCS Teaching Formal Methods workshop",
  publisher = "BCS",
  title = "Supplementing the understanding of Z: a formal approach to database design",
  year = "2003",
}



@inproceedings{Us:CogDev,
  address = "Berlin",
  author = "Owen Traynor and Dan Hazel and Peter Kearney and Andrew Martin and Ray Nickson and Luke Wildman",
  booktitle = "Algebraic Methodology and Software Technology",
  editor = "Michael Johnson",
  isbn = "3-540-63888-1",
  issn = "0302-9743",
  month = "dec",
  note = "6th International conference, AMAST'97, Sydney, Australia",
  pages = "586--591",
  publisher = "Springer-Verlag",
  series = "LNCS",
  title = "The {Cogito} development system",
  volume = "1349",
  year = "1997",
}



@inproceedings{me:infinite,
  author = "Andrew Martin",
  booktitle = "Proceedings of Australian Refinement Workshop",
  publisher = "University of Queensland",
  title = "Infinite Lists for Specifying Functional Programs in {Z}",
  url = "http://www.it.uq.edu.au/MENU/WORKSHOPS-SEMINARS-CONFERENCES/WORKSHOPS/Martin.ps.gz",
  year = "1996",
}



@techreport{RR-07-01,
  abstract = "Prospective readers can quickly determine whether a document is relevant to their information need if the significant phrases (or keyphrases) in this document are provided. Although keyphrases are useful, not many documents have keyphrases assigned to them, and manually assigning keyphrases to existing documents is costly. Therefore, there is a need for automatic keyphrase extraction. This report introduces a new domain independent keyphrase extraction algorithm. The algorithm approaches the problem of keyphrase extraction as a classification task, and uses a combination of statistical and computational linguistics techniques, a new set of attributes, and a new machine learning method to distinguish keyphrases from non-keyphrases. The experiments indicate that this algorithm performs at least as well as other keyphrase extraction tools and that it significantly outperforms Microsoft Word 2000's AutoSummarize feature.",
  author = "Yuan J.Lui",
  institution = "Oxford University Computing Laboratory",
  number = "RR-07-01",
  title = "Learning to Extract Significant Phrases from Text",
  year = "2007",
}