Abstract
In this paper we present an algorithm for automatic extraction of textual elements, namely titles and full text, associated with news stories in news web pages. We propose a supervised machine learning classification technique based on the use of a Support Vector Machine (SVM) classifier to extract the desired textual elements. The technique uses internal structural features of a webpage without relying on the Document Object Model to which many content authors fail to adhere. The classifier uses a set of features which rely on the length of text, the percentage of hypertext, etc. The resulting classifier is nearly perfect on previously unseen news pages from different sites. The proposed technique is successfully employed in Alzoa.com, which is the largest Arabic news aggregator on the web.- Anthology ID:
- L08-1300
- Volume:
- Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
- Month:
- May
- Year:
- 2008
- Address:
- Marrakech, Morocco
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/407_paper.pdf
- DOI:
- Cite (ACL):
- Hossam Ibrahim, Kareem Darwish, and Abdel-Rahim Madany. 2008. Automatic Extraction of Textual Elements from News Web Pages. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
- Cite (Informal):
- Automatic Extraction of Textual Elements from News Web Pages (Ibrahim et al., LREC 2008)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/407_paper.pdf