Removing Spam from Web Corpora Through Supervised Learning and Semi-manual Classification of Web Sites

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00117841" target="_blank" >RIV/00216224:14330/20:00117841 - isvavai.cz</a>

Výsledek na webu

<a href="https://nlp.fi.muni.cz/raslan/raslan20.pdf#page=121" target="_blank" >https://nlp.fi.muni.cz/raslan/raslan20.pdf#page=121</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Removing Spam from Web Corpora Through Supervised Learning and Semi-manual Classification of Web Sites

Popis výsledku v původním jazyce

Internet spam is a major issue hindering the usefulness of web corpora. Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be cleaned. In this paper, two experiments of non-text removal based on supervised learning are presented. First, an improvement of corpus based language analyses of selected words achieved by a supervised classifier is shown on an English web corpus. Then, a semi-manual approach of obtaining samples of non-text web pages in Estonian is introduced. This strategy makes the supervised learning process more efficient. The result spam classifiers are tuned for high recall at the cost of precision to remove as much non-text as possible. The evaluation shows the classifiers reached the recall of 71 % and 97 % for English and Estonian web corpus, respectively. A technique for avoiding spammed web sites by measuring the distance of web pages from trustworthy sites is studied too.

Název v anglickém jazyce

Removing Spam from Web Corpora Through Supervised Learning and Semi-manual Classification of Web Sites

Popis výsledku anglicky

Internet spam is a major issue hindering the usefulness of web corpora. Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be cleaned. In this paper, two experiments of non-text removal based on supervised learning are presented. First, an improvement of corpus based language analyses of selected words achieved by a supervised classifier is shown on an English web corpus. Then, a semi-manual approach of obtaining samples of non-text web pages in Estonian is introduced. This strategy makes the supervised learning process more efficient. The result spam classifiers are tuned for high recall at the cost of precision to remove as much non-text as possible. The evaluation shows the classifiers reached the recall of 71 % and 97 % for English and Estonian web corpus, respectively. A technique for avoiding spammed web sites by measuring the distance of web pages from trustworthy sites is studied too.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/LM2018101" target="_blank" >LM2018101: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Fourteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2020

ISBN

9788026316008

ISSN

2336-4289

e-ISSN

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Počet stran výsledku

11

Strana od-do

113-123

Název nakladatele

Tribun 2020

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

1. 1. 2020

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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