Abstract
Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains, like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles, relying on Natural Language Processing (NLP) and dictionaries-based techniques. The results of our experiments confirm that, by considering domain-oriented features, it is possible to improve existing solutions, mainly with those articles that other approaches have less correctly classified.
Original language | English |
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Title of host publication | Web Engineering : 16th International Conference, ICWE 2016, Lugano, Switzerland, June 6-9, 2016. Proceedings |
Number of pages | 9 |
Volume | 9671 |
Publisher | Springer |
Publication date | 2016 |
Pages | 448-456 |
ISBN (Print) | 978-3-319-38790-1 |
ISBN (Electronic) | 978-3-319-38791-8 |
DOIs | |
Publication status | Published - 2016 |
Event | The 16th International Conference on Web Engineering - USI Lugano, Switzerland Duration: 6 Jun 2016 → 9 Jun 2016 Conference number: 16 |
Conference
Conference | The 16th International Conference on Web Engineering |
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Number | 16 |
Country/Territory | Switzerland |
City | USI Lugano |
Period | 06/06/2016 → 09/06/2016 |
Series | Lecture Notes in Computer Science |
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ISSN | 0302-9743 |
Keywords
- Information Systems Applications (incl. Internet)
- Information Storage and Retrieval
- Software Engineering
- Computer Appl. in Administrative Data Processing
- User Interfaces and Human Computer Interaction
- Artificial Intelligence (incl. Robotics)