The influence of feature representation of text on the performance of document classification

Martinčić-Ipšić, Sanda (avtor), Miličić, Tanja (avtor), Todorovski, Ljupčo (avtor)

In this paper we perform a comparative analysis of three models for a feature representation of text documents in the context of document classification. In particular, we consider the most often used family of bag-of-words models, the recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based models that have been rarely considered for the representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated with the three models and their variants. Multi-objective rankings are proposed as the framework for multi-criteria comparative analysis of the results. Finally, the results of the empirical comparison show that the commonly used bag-of-words model has a performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large documents.

• Jezik: Angleški jezik
• Ključne besede: document segmentation, bag-of-words, word2vec, doc2vec, graph-of-words, complex networks, document classification
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FU - Fakulteta za upravo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2019
• Št. strani: 27 str.
• Številčenje: Vol. 9, iss. 4, art. 743
• PID: 20.500.12556/RUL-131966
• UDK: 004:78
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app9040743
• COBISS.SI-ID: 5274286
• Datum objave v RUL: 07.10.2021

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 20.02.2019

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, razvrščanje besedil, vreča besed, word2vec, doc2vec, graf besed, kompleksna omrežja

Projekti

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: University of Rijeka
• Številka projekta: 13.13.2.2.07
• Akronim: LangNet

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P5-0093
• Naslov: Razvoj sistema učinkovite in uspešne javne uprave