Vector representation of context networks of latent topics

Kód výsledku v IS VaVaI

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Nalezeny alternativní kódy

RIV/68407700:21240/13:00214138

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Vector representation of context networks of latent topics

Popis výsledku v původním jazyce

Transforming of text documents to real vectors is an essential step for text mining tasks such as classification, clustering and information retrieval. The extracted vectors serve as inputs for data mining models. Large vocabularies of natural languagesimply a high dimensionality of input vectors; hence a substantial dimensionality reduction has to be made. We propose a new approach to a vector representation of text documents. Our representation takes into account an order of latent topics that generate observed words; an extracted document vector includes information about the adjacency of words in a document. We experimentally proved that the proposed representation enables to build document classifiers of higher accuracy using shorter document vectors. Short but informative document vectors enable to save memory for storing data, to use simpler models that learn faster and to significantly reduce an overfit effect.

Název v anglickém jazyce

Vector representation of context networks of latent topics

Popis výsledku anglicky

Transforming of text documents to real vectors is an essential step for text mining tasks such as classification, clustering and information retrieval. The extracted vectors serve as inputs for data mining models. Large vocabularies of natural languagesimply a high dimensionality of input vectors; hence a substantial dimensionality reduction has to be made. We propose a new approach to a vector representation of text documents. Our representation takes into account an order of latent topics that generate observed words; an extracted document vector includes information about the adjacency of words in a document. We experimentally proved that the proposed representation enables to build document classifiers of higher accuracy using shorter document vectors. Short but informative document vectors enable to save memory for storing data, to use simpler models that learn faster and to significantly reduce an overfit effect.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the World Congress on Engineering 2013

ISBN

978-988-19251-0-7

ISSN

2078-0958

e-ISSN

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Počet stran výsledku

5

Strana od-do

286-290

Název nakladatele

Newswood Limited - International Association of Engineers

Místo vydání

Hong Kong

Místo konání akce

London

Datum konání akce

3. 5. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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