Predicting the effectiveness of pattern-based entity extractor inference
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Bartoli:2016:ASC,
-
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 = "http://www.sciencedirect.com/science/article/pii/S1568494616302241",
-
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
estimation",
- }
Genetic Programming entries for
Alberto Bartoli
Andrea De Lorenzo
Eric Medvet
Fabiano Tarlao
Citations