Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15410%2F20%3A73606106" target="_blank" >RIV/61989592:15410/20:73606106 - isvavai.cz</a>

Výsledek na webu

<a href="https://obd.upol.cz/id_publ/333185992" target="_blank" >https://obd.upol.cz/id_publ/333185992</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMAP49528.2020.9248454" target="_blank" >10.1109/SMAP49528.2020.9248454</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm

Popis výsledku v původním jazyce

A common task in a world of natural language processing is text classification useful for e.g.spam filters, documents sorting, science articles classification or plagiarism detection. This can still be done best and most accurately by human, on the other hand, we can of ten accept certain error in the classification in exchange for its speed. Here, natural language processing mechanism transforms the text in natural language to a form understandable by a classifier such as K-Nearest Neighbour, Decision Trees, Artificial Neural Network or Support Vector Machines. We can also use thishuman element to help automated classification to improve its accuracy by means of crowdsourcing. This work deals with classification of text documents and its improvement through crowdsourcing. Itsgoal is to design and implement text documents classifier prototype based on documents similarityand to design evaluation and crowdsourcing-based classification improvement mechanism. For classification the N-grams algorithm has been chosen, which was implemented in Java. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful. We have tested our approach on two data sets with promising preliminary results even across different languages. This led to a real-world implementation started at the beginning of 2019 in cooperation of two universities: VšB-TUO and OSU.

Název v anglickém jazyce

Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm

Popis výsledku anglicky

A common task in a world of natural language processing is text classification useful for e.g.spam filters, documents sorting, science articles classification or plagiarism detection. This can still be done best and most accurately by human, on the other hand, we can of ten accept certain error in the classification in exchange for its speed. Here, natural language processing mechanism transforms the text in natural language to a form understandable by a classifier such as K-Nearest Neighbour, Decision Trees, Artificial Neural Network or Support Vector Machines. We can also use thishuman element to help automated classification to improve its accuracy by means of crowdsourcing. This work deals with classification of text documents and its improvement through crowdsourcing. Itsgoal is to design and implement text documents classifier prototype based on documents similarityand to design evaluation and crowdsourcing-based classification improvement mechanism. For classification the N-grams algorithm has been chosen, which was implemented in Java. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful. We have tested our approach on two data sets with promising preliminary results even across different languages. This led to a real-world implementation started at the beginning of 2019 in cooperation of two universities: VšB-TUO and OSU.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

SMAP 2020 - 15th International Workshop on Semantic and Social Media Adaptation and Personalization

ISBN

978-1-72815-919-5

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-6

Název nakladatele

IEEE Computer Society Press

Místo vydání

New York

Místo konání akce

Zakynthos

Datum konání akce

29. 10. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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