@inproceedings{piasecki-etal-2012-recognition,
title = "Recognition of {P}olish Derivational Relations Based on Supervised Learning Scheme",
author = "Piasecki, Maciej and
Ramocki, Radoslaw and
Maziarz, Marek",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/926_Paper.pdf",
pages = "916--922",
abstract = "The paper presents construction of {\textbackslash}emph{Derywator} -- a language tool for the recognition of Polish derivational relations. It was built on the basis of machine learning in a way following the bootstrapping approach: a limited set of derivational pairs described manually by linguists in plWordNet is used to train {\textbackslash}emph{Derivator}. The tool is intended to be applied in semi-automated expansion of plWordNet with new instances of derivational relations. The training process is based on the construction of two transducers working in the opposite directions: one for prefixes and one for suffixes. Internal stem alternations are recognised, recorded in a form of mapping sequences and stored together with transducers. Raw results produced by {\textbackslash}emph{Derivator} undergo next corpus-based and morphological filtering. A set of derivational relations defined in plWordNet is presented. Results of tests for different derivational relations are discussed. A problem of the necessary corpus-based semantic filtering is analysed. The presented tool depends to a very little extent on the hand-crafted knowledge for a particular language, namely only a table of possible alternations and morphological filtering rules must be exchanged and it should not take longer than a couple of working days.",
}
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<abstract>The paper presents construction of \textbackslashemphDerywator – a language tool for the recognition of Polish derivational relations. It was built on the basis of machine learning in a way following the bootstrapping approach: a limited set of derivational pairs described manually by linguists in plWordNet is used to train \textbackslashemphDerivator. The tool is intended to be applied in semi-automated expansion of plWordNet with new instances of derivational relations. The training process is based on the construction of two transducers working in the opposite directions: one for prefixes and one for suffixes. Internal stem alternations are recognised, recorded in a form of mapping sequences and stored together with transducers. Raw results produced by \textbackslashemphDerivator undergo next corpus-based and morphological filtering. A set of derivational relations defined in plWordNet is presented. Results of tests for different derivational relations are discussed. A problem of the necessary corpus-based semantic filtering is analysed. The presented tool depends to a very little extent on the hand-crafted knowledge for a particular language, namely only a table of possible alternations and morphological filtering rules must be exchanged and it should not take longer than a couple of working days.</abstract>
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%0 Conference Proceedings
%T Recognition of Polish Derivational Relations Based on Supervised Learning Scheme
%A Piasecki, Maciej
%A Ramocki, Radoslaw
%A Maziarz, Marek
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F piasecki-etal-2012-recognition
%X The paper presents construction of \textbackslashemphDerywator – a language tool for the recognition of Polish derivational relations. It was built on the basis of machine learning in a way following the bootstrapping approach: a limited set of derivational pairs described manually by linguists in plWordNet is used to train \textbackslashemphDerivator. The tool is intended to be applied in semi-automated expansion of plWordNet with new instances of derivational relations. The training process is based on the construction of two transducers working in the opposite directions: one for prefixes and one for suffixes. Internal stem alternations are recognised, recorded in a form of mapping sequences and stored together with transducers. Raw results produced by \textbackslashemphDerivator undergo next corpus-based and morphological filtering. A set of derivational relations defined in plWordNet is presented. Results of tests for different derivational relations are discussed. A problem of the necessary corpus-based semantic filtering is analysed. The presented tool depends to a very little extent on the hand-crafted knowledge for a particular language, namely only a table of possible alternations and morphological filtering rules must be exchanged and it should not take longer than a couple of working days.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/926_Paper.pdf
%P 916-922
Markdown (Informal)
[Recognition of Polish Derivational Relations Based on Supervised Learning Scheme](http://www.lrec-conf.org/proceedings/lrec2012/pdf/926_Paper.pdf) (Piasecki et al., LREC 2012)
ACL