Active learning approaches for learning regular expressions with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/sac/BartoliLMT16,
  author =       "Alberto Bartoli and Andrea {De Lorenzo} and 
                 Eric Medvet and Fabiano Tarlao",
  title =        "Active learning approaches for learning regular
                 expressions with genetic programming",
  bibdate =      "2016-06-06",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/sac/sac2016.html#BartoliLMT16",
  booktitle =    "Proceedings of the 31st Annual {ACM} Symposium on
                 Applied Computing, Pisa, Italy, April 4-8, 2016",
  publisher =    "ACM",
  year =         "2016",
  editor =       "Sascha Ossowski",
  isbn13 =       "978-1-4503-3739-7",
  pages =        "97--102",
  keywords =     "genetic algorithms, genetic programming, entity
                 extraction, information extraction, machine learning,
                 programming by examples",
  DOI =          "doi:10.1145/2851613.2851668",
  abstract =     "We consider the long-standing problem of the automatic
                 generation of regular expressions for text extraction,
                 based solely on examples of the desired behaviour. We
                 investigate several active learning approaches in which
                 the user annotates only one desired extraction and then
                 merely answers extraction queries generated by the
                 system.

                 The resulting framework is attractive because it is the
                 system, not the user, which digs out the data in search
                 of the samples most suitable to the specific learning
                 task. We tailor our proposals to a state-of-the-art
                 learner based on Genetic Programming and we assess them
                 experimentally on a number of challenging tasks of
                 realistic complexity. The results indicate that active
                 learning is indeed a viable framework in this
                 application domain and may thus significantly decrease
                 the amount of costly annotation effort required.",
}

Genetic Programming entries for Alberto Bartoli Andrea De Lorenzo Eric Medvet Fabiano Tarlao

Citations