About: General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. (en)
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 (en)
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 (en)
skos:notation	RIV/49777513:23520/14:43919601!RIV15-GA0-23520___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(GBP103/12/G084), S
http://linked.open...iv/cisloPeriodika	2
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Lehečka, Jan Skorkovská, Lucie Pražák, Aleš Ircing, Pavel Švec, Jan Hoidekr, Jan Stanislav, Petr Vavruška, Jan
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	18022
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/14:43919601
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	duplicity detection; topic identification; language modeling; text data mining (en)
http://linked.open.../riv/klicoveSlovo	text data mining language modeling topic identification duplicity detection
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[91B82CD2E095]
http://linked.open...i/riv/nazevZdroje	Language Resources and Evaluation
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	8 (xsd:int)
http://linked.open...cetTvurcuVysledku	8 (xsd:int)
http://linked.open...vavai/riv/projekt	Center for Large Scale Multi-modal Data Interpretation
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	48
http://linked.open...iv/tvurceVysledku	Ircing, Pavel Lehečka, Jan Pražák, Aleš Skorkovská, Lucie Švec, Jan Hoidekr, Jan Stanislav, Petr Vavruška, Jan
http://linked.open...ain/vavai/riv/wos	000335779200003
issn	1574-020X
number of pages	22 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/s10579-013-9246-z
http://localhost/t...ganizacniJednotka	23520

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 47 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software