Genetic programming-based feature learning for question answering

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Khodadi:2016:IPM,
  author =       "Iman Khodadi and Mohammad Saniee Abadeh",
  title =        "Genetic programming-based feature learning for
                 question answering",
  journal =      "Information Processin \& Management",
  volume =       "52",
  number =       "2",
  pages =        "340--357",
  year =         "2016",
  ISSN =         "0306-4573",
  DOI =          "doi:10.1016/j.ipm.2015.09.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0306457315001193",
  abstract =     "Question Answering (QA) systems are developed to
                 answer human questions. In this paper, we have proposed
                 a framework for answering definitional and factoid
                 questions, enriched by machine learning and
                 evolutionary methods and integrated in a web-based QA
                 system. Our main purpose is to build new features by
                 combining state-of-the-art features with arithmetic
                 operators. To accomplish this goal, we have presented a
                 Genetic Programming (GP)-based approach. The exact GP
                 duty is to find the most promising formulas, made by a
                 set of features and operators, which can accurately
                 rank paragraphs, sentences, and words. We have also
                 developed a QA system in order to test the new
                 features. The input of our system is texts of documents
                 retrieved by a search engine. To answer definitional
                 questions, our system performs paragraph ranking and
                 returns the most related paragraph. Moreover, in order
                 to answer factoid questions, the system evaluates
                 sentences of the filtered paragraphs ranked by the
                 previous module of our framework. After this phase, the
                 system extracts one or more words from the ranked
                 sentences based on a set of hand-made patterns and
                 ranks them to find the final answer. We have used Text
                 Retrieval Conference (TREC) QA track questions, web
                 data, and AQUAINT and AQUAINT-2 datasets for training
                 and testing our system. Results show that the learned
                 features can perform a better ranking in comparison
                 with other evaluation formulas.",
  keywords =     "genetic algorithms, genetic programming, Question
                 Answering (QA), Feature learning, Feature weight
                 learning, Factoid questions, Information Extraction
                 (IE)",
}

Genetic Programming entries for Iman Khodadi Mohammad Saniee Abadeh

Citations