Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction

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

  author =       "Alberto Bartoli and Andrea {De Lorenzo} and 
                 Eric Medvet and Fabiano Tarlao and Marco Virgolin",
  title =        "Evolutionary Learning of Syntax Patterns for Genic
                 Interaction Extraction",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1183--1190",
  keywords =     "genetic algorithms, genetic programming, Real World
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754706",
  DOI =          "doi:10.1145/2739480.2754706",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "There is an increasing interest in the development of
                 techniques for automatic relation extraction from
                 unstructured text. The biomedical domain, in
                 particular, is a sector that may greatly benefit from
                 those techniques due to the huge and ever increasing
                 amount of scientific publications describing observed
                 phenomena of potential clinical interest. In this
                 paper, we consider the problem of automatically
                 identifying sentences that contain interactions between
                 genes and proteins, based solely on a dictionary of
                 genes and proteins and a small set of sample sentences
                 in natural language. We propose an evolutionary
                 technique for learning a classifier that is capable of
                 detecting the desired sentences within scientific
                 publications with high accuracy. The key feature of our
                 proposal, that is internally based on Genetic
                 Programming, is the construction of a model of the
                 relevant syntax patterns in terms of standard
                 part-of-speech annotations. The model consists of a set
                 of regular expressions that are learned automatically
                 despite the large alphabet size involved. We assess our
                 approach on two realistic datasets and obtain 74percent
                 accuracy, a value sufficiently high to be of practical
                 interest and that is in line with significant baseline
  notes =        "Also known as \cite{2754706} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

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