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Pregled bibliografske jedinice broj: 700038

Non-Standard Words as Features for Text Categorization


Beliga, Slobodan; Martinčić-Ipšić, Sanda
Non-Standard Words as Features for Text Categorization // MIPRO-CIS / Ribarić, Slobodan ; Budin, Andrea (ur.).
Opatija: MIPRO, 2014. str. 1415-1419 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Non-Standard Words as Features for Text Categorization

Autori
Beliga, Slobodan ; Martinčić-Ipšić, Sanda

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
MIPRO-CIS / Ribarić, Slobodan ; Budin, Andrea - Opatija : MIPRO, 2014, 1415-1419

ISBN
978-953-233-078-6

Skup
IEEE 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2014)

Mjesto i datum
Opatija, Croatia, 26-30.5.2014

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Text categorization; non-standard words; collection representation; features; accuracy

Sažetak
This paper presents the categorization of Croatian texts using Non-Standard Words (NSW) as features. Non-Standard Words are: numbers, dates, acronyms, abbreviations, currency, etc. NSWs in Croatian language are determined according to Croatian NSW taxonomy. For the purpose of this research, 390 text documents were collected and formed the SKIPEZ collection with 6 classes: official, literary, informative, popular, educational and scientific. Text categorization experiment was conducted on three different representations of the SKIPEZ collection: in the first representation, the frequencies of NSWs are used as features ; in the second representation, the statistic measures of NSWs (variance, coefficient of variation, standard deviation, etc.) are used as features ; while the third representation combines the first two feature sets. Naive Bayes, CN2, C4.5, kNN, Classification Trees and Random Forest algorithms were used in text categorization experiments. The best categorization results are achieved using the first feature set (NSW frequencies) with the categorization accuracy of 87%. This suggests that the NSWs should be considered as features in highly inflectional languages, such as Croatian. NSW based features reduce the dimensionality of the feature space without standard lemmatization procedures, and therefore the bag-of-NSWs should be considered for further Croatian texts categorization experiments.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



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