Active Learning of Regular Expressions for Entity Extraction

Created by W.Langdon from gp-bibliography.bib Revision:1.7410

@Article{Bartoli:2017:ieeeTC,

• author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao",
• journal = "IEEE Transactions on Cybernetics",
• title = "Active Learning of Regular Expressions for Entity Extraction",
• year = "2018",
• volume = "48",
• number = "3",
• pages = "1067--1080",
• month = mar,
• keywords = "genetic algorithms, genetic programming, automatic programming, evolutionary computation, inference mechanisms, man machine systems, semisupervised learning, text processing",
• ISSN = "2168-2267",
• DOI = "doi:10.1109/TCYB.2017.2680466",
• size = "14 pages",
• abstract = "We consider the automatic synthesis of an entity extractor, in the form of a regular expression, from examples of the desired extractions in an unstructured text stream. This is a long-standing problem for which many different approaches have been proposed, which all require the preliminary construction of a large dataset fully annotated by the user. we propose an active learning approach aimed at minimizing the user annotation effort: the user annotates only one desired extraction and then merely answers extraction queries generated by the system. During the learning process, the system digs into the input text for selecting the most appropriate extraction query to be submitted to the user in order to improve the current extractor. We construct candidate solutions with genetic programming (GP) and select queries with a form of querying-by-committee, i.e., based on a measure of disagreement within the best candidate solutions. All the components of our system are carefully tailored to the peculiarities of active learning with GP and of entity extraction from unstructured text. We evaluate our proposal in depth, on a number of challenging datasets and based on a realistic estimate of the user effort involved in answering each single query. The results demonstrate high accuracy with significant savings in terms of computational effort, annotated characters, and execution time over a state-of-the-art baseline.",
• notes = "Also known as \cite{7886274}",

}

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

Citations