Empirical Text Mining for Genre Detection
(2012) In WEBIST 1. p.733-737- Abstract
- In this paper, we report on a preliminary study we carried out for identifying patterns that characterize the genre type of Greek texts. In the course of our study, we address four distinct genre types, we record their observable stylistic elements and we indicate their exploitation for automatic genre-based document classi-fication. The findings of our study demonstrate that texts contain lexical features with discriminative power as far as genre is concerned, however modeling those features so that they can be explored by computer-based applications is still in early stages.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/db214761-4297-4c20-abf0-32f2325d4ded
- author
- Simaki, Vasiliki LU ; Stamou, Sofia and Kirtsis, Nikolaos
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Genre Detection, Annotation, Human Study
- host publication
- Proceedings of the 8th International Conference on Web Information Systems and Technologies
- series title
- WEBIST
- editor
- Krempels, Karl-Heinz and Cordeiro, José
- volume
- 1
- pages
- 733 - 737
- publisher
- SciTePress
- external identifiers
-
- scopus:84864878316
- ISBN
- 978-989-8565-08-2
- DOI
- 10.5220/0003956207330737
- language
- English
- LU publication?
- no
- id
- db214761-4297-4c20-abf0-32f2325d4ded
- date added to LUP
- 2017-06-02 19:35:07
- date last changed
- 2022-02-07 05:39:30
@inproceedings{db214761-4297-4c20-abf0-32f2325d4ded, abstract = {{In this paper, we report on a preliminary study we carried out for identifying patterns that characterize the genre type of Greek texts. In the course of our study, we address four distinct genre types, we record their observable stylistic elements and we indicate their exploitation for automatic genre-based document classi-fication. The findings of our study demonstrate that texts contain lexical features with discriminative power as far as genre is concerned, however modeling those features so that they can be explored by computer-based applications is still in early stages.}}, author = {{Simaki, Vasiliki and Stamou, Sofia and Kirtsis, Nikolaos}}, booktitle = {{Proceedings of the 8th International Conference on Web Information Systems and Technologies}}, editor = {{Krempels, Karl-Heinz and Cordeiro, José}}, isbn = {{978-989-8565-08-2}}, keywords = {{Genre Detection; Annotation; Human Study}}, language = {{eng}}, pages = {{733--737}}, publisher = {{SciTePress}}, series = {{WEBIST}}, title = {{Empirical Text Mining for Genre Detection}}, url = {{http://dx.doi.org/10.5220/0003956207330737}}, doi = {{10.5220/0003956207330737}}, volume = {{1}}, year = {{2012}}, }