Semi-Manual Annotation of Topics and Genres in Web Corpora :
The Cheap and Fast Way

D 2022

Semi-Manual Annotation of Topics and Genres in Web Corpora : The Cheap and Fast Way

SUCHOMEL, Vít and Jan KRAUS

Basic information

Original name

Semi-Manual Annotation of Topics and Genres in Web Corpora : The Cheap and Fast Way

Authors

SUCHOMEL, Vít (203 Czech Republic, guarantor, belonging to the institution) and Jan KRAUS (203 Czech Republic)

Edition

Brno, Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022, p. 141-148, 8 pp. 2022

Publisher

Tribun EU

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

Domovská stránka workshopu Plný text

RIV identification code

RIV/00216224:14330/22:00127492

Organization unit

Faculty of Informatics

ISBN

978-80-263-1752-4

ISSN

Keywords in English

web corpus; text corpus; topic; genre; text annotation

Změněno: 15/5/2024 09:27, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

In this paper we present a cheap and fast semi-manual approach to annotation of topics and genres in web corpora. The main feature of our method is assigning the same topic or genre label to all web pages coming from websites most represented in the corpus. We assume that web pages within a site share the topic of the whole domain. According to the evaluation of texts coming from sites that were manually assigned a topic label, our hypothesis holds in 92 % of cases. In other words, the noise in these semi-manually labelled web pages is just 8 %. That is clean enough to train a classifier of texts from websites not seen in the process. The procedure of fast manual topic and genre labelling of web domains is described in this paper. Recommendations for training a topic or genre classifier using semi-manually labelled texts from large websites follow.

Links

LM2018101, research and development project

Name: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy (Acronym: LINDAT/CLARIAH-CZ)

Investor: Ministry of Education, Youth and Sports of the CR

Citovat

SUCHOMEL, Vít and Jan KRAUS. Semi-Manual Annotation of Topics and Genres in Web Corpora : The Cheap and Fast Way. In Aleš Horák, Pavel Rychlý, Adam Rambousek. Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022. Brno: Tribun EU, 2022, p. 141-148. ISBN 978-80-263-1752-4.

@inproceedings{2240321,
   author = {Suchomel, Vít and Kraus, Jan},
   address = {Brno},
   booktitle = {Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022},
   editor = {Aleš Horák, Pavel Rychlý, Adam Rambousek},
   keywords = {web corpus; text corpus; topic; genre; text annotation},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno},
   isbn = {978-80-263-1752-4},
   pages = {141-148},
   publisher = {Tribun EU},
   title = {Semi-Manual Annotation of Topics and Genres in Web Corpora : The Cheap and Fast Way},
   url = {https://raslan2022.nlp-consulting.net/},
   year = {2022}
}

TY  - JOUR
ID  - 2240321
AU  - Suchomel, Vít - Kraus, Jan
PY  - 2022
TI  - Semi-Manual Annotation of Topics and Genres in Web Corpora : The Cheap and Fast Way
PB  - Tribun EU
CY  - Brno
SN  - 9788026317524
KW  - web corpus
KW  - text corpus
KW  - topic
KW  - genre
KW  - text annotation
UR  - https://raslan2022.nlp-consulting.net/
N2  - In this paper we present a cheap and fast semi-manual approach to annotation of topics and genres in web corpora. The main feature of our method is assigning the same topic or genre label to all web pages coming from websites most represented in the corpus. We assume that web pages within a site share the topic of the whole domain. According to the evaluation of texts coming from sites that were manually assigned a topic label, our hypothesis holds in 92 % of cases. In other words, the noise in these semi-manually labelled web pages is just 8 %. That is clean enough to train a classifier of texts from websites not seen in the process. The procedure of fast manual topic and genre labelling of web domains is described in this paper. Recommendations for training a topic or genre classifier using semi-manually labelled texts from large websites follow.
ER  -

SUCHOMEL, Vít and Jan KRAUS. Semi-Manual Annotation of Topics and Genres in Web Corpora : The Cheap and Fast Way. In Aleš Horák, Pavel Rychlý, Adam Rambousek. \textit{Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022}. Brno: Tribun EU, 2022, p.~141-148. ISBN~978-80-263-1752-4.

Detailed Information on Publication Record