Syntactical Similarity Learning by means of Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Bartoli:2016:PPSN,
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author = "Alberto Bartoli and Andrea {De Lorenzo} and
Eric Medvet and Fabiano Tarlao",
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title = "Syntactical Similarity Learning by means of
Grammatical Evolution",
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booktitle = "14th International Conference on Parallel Problem
Solving from Nature",
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year = "2016",
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editor = "Julia Handl and Emma Hart and Peter R. Lewis and
Manuel Lopez-Ibanez and Gabriela Ochoa and
Ben Paechter",
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volume = "9921",
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series = "LNCS",
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pages = "260--269",
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address = "Edinburgh",
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month = "17-21 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-3-319-45823-6",
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DOI = "doi:10.1007/978-3-319-45823-6_24",
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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
index.",
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notes = "PPSN2016",
- }
Genetic Programming entries for
Alberto Bartoli
Andrea De Lorenzo
Eric Medvet
Fabiano Tarlao
Citations