Predicting the effectiveness of pattern-based entity extractor inference

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alberto Bartoli and Andrea {De Lorenzo} and 
                 Eric Medvet and Fabiano Tarlao",
  title =        "Predicting the effectiveness of pattern-based entity
                 extractor inference",
  journal =      "Applied Soft Computing",
  volume =       "46",
  pages =        "398--406",
  year =         "2016",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.05.023",
  URL =          "",
  abstract =     "An essential component of any workflow leveraging
                 digital data consists in the identification and
                 extraction of relevant patterns from a data stream. We
                 consider a scenario in which an extraction inference
                 engine generates an entity extractor automatically from
                 examples of the desired behaviour, which take the form
                 of user-provided annotations of the entities to be
                 extracted from a dataset. We propose a methodology for
                 predicting the accuracy of the extractor that may be
                 inferred from the available examples. We propose
                 several prediction techniques and analyse
                 experimentally our proposals in great depth, with
                 reference to extractors consisting of regular
                 expressions. The results suggest that reliable
                 predictions for tasks of practical complexity may
                 indeed be obtained quickly and without actually
                 generating the entity extractor.",
  keywords =     "genetic algorithms, genetic programming, String
                 similarity metrics, Information extraction, Hardness

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