Syntactical Similarity Learning by means of Grammatical Evolution

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 =        "Syntactical Similarity Learning by means of
                 Grammatical Evolution",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "260--269",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_24",
  abstract =     "Several research efforts have shown that a similarity
                 function synthesized from examples may capture an
                 application-specific similarity criterion in a way that
                 fits the application needs more effectively than a
                 generic distance definition. In this work, we propose a
                 similarity learning algorithm tailored to problems of
                 syntax-based entity extraction from unstructured text
                 streams. The algorithm takes in input pairs of strings
                 along with an indication of whether they adhere or not
                 adhere to the same syntactic pattern. Our approach is
                 based on Grammatical Evolution and explores
                 systematically a similarity definition space including
                 all functions that may be expressed with a specialized,
                 simple language that we have defined for this purpose.
                 We assessed our proposal on patterns representative of
                 practical applications. The results suggest that the
                 proposed approach is indeed feasible and that the
                 learned similarity function is more effective than the
                 Levenshtein distance and the Jaccard similarity
  notes =        "PPSN2016",

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