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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F14%3A43919601%21RIV15-GA0-23520___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
The paper describes a general framework for mining large amounts of text data from a defined set of Web pages. The acquired data are meant to constitute a corpus for training robust and reliable language models and thus the framework needs to also incorporate algorithms for appropriate text processing and duplicity detection in order to secure quality and consistency of the data. As we expect the resulting corpus to be very large, we have also implemented topic detection algorithms that allow us to automatically select subcorpora for domain-specific language models. The description of the framework architecture and the implemented algorithms is complemented with a detailed evaluation section. It analyses the basic properties of the gathered Czech corpus containing more than one billion text tokens collected using the described framework, shows the results of the topic detection methods and finally also describes the design and outcomes of the automatic speech recognition experiments with domain-specific language models estimated from the collected data. The paper describes a general framework for mining large amounts of text data from a defined set of Web pages. The acquired data are meant to constitute a corpus for training robust and reliable language models and thus the framework needs to also incorporate algorithms for appropriate text processing and duplicity detection in order to secure quality and consistency of the data. As we expect the resulting corpus to be very large, we have also implemented topic detection algorithms that allow us to automatically select subcorpora for domain-specific language models. The description of the framework architecture and the implemented algorithms is complemented with a detailed evaluation section. It analyses the basic properties of the gathered Czech corpus containing more than one billion text tokens collected using the described framework, shows the results of the topic detection methods and finally also describes the design and outcomes of the automatic speech recognition experiments with domain-specific language models estimated from the collected data.
dcterms:title
General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes
skos:prefLabel
General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes
skos:notation
RIV/49777513:23520/14:43919601!RIV15-GA0-23520___
n3:aktivita
n12:S n12:P
n3:aktivity
P(GBP103/12/G084), S
n3:cisloPeriodika
2
n3:dodaniDat
n11:2015
n3:domaciTvurceVysledku
n4:5789435 n4:1145177 n4:3088235 n4:2389916 n4:5283183 n4:4979222 n4:8780943 n4:2152517
n3:druhVysledku
n14:J
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
18022
n3:idVysledku
RIV/49777513:23520/14:43919601
n3:jazykVysledku
n6:eng
n3:klicovaSlova
duplicity detection; topic identification; language modeling; text data mining
n3:klicoveSlovo
n10:text%20data%20mining n10:topic%20identification n10:language%20modeling n10:duplicity%20detection
n3:kodStatuVydavatele
NL - Nizozemsko
n3:kontrolniKodProRIV
[91B82CD2E095]
n3:nazevZdroje
Language Resources and Evaluation
n3:obor
n5:IN
n3:pocetDomacichTvurcuVysledku
8
n3:pocetTvurcuVysledku
8
n3:projekt
n7:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n11:2014
n3:svazekPeriodika
48
n3:tvurceVysledku
Pražák, Aleš Stanislav, Petr Ircing, Pavel Vavruška, Jan Švec, Jan Skorkovská, Lucie Lehečka, Jan Hoidekr, Jan
n3:wos
000335779200003
s:issn
1574-020X
s:numberOfPages
22
n18:doi
10.1007/s10579-013-9246-z
n9:organizacniJednotka
23520