The nature of statistical learning theory
The nature of statistical learning theory
A statistical model for scientific readability
Proceedings of the tenth international conference on Information and knowledge management
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Automatic recognition of reading levels from user queries
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting reading difficulty with statistical language models
Journal of the American Society for Information Science and Technology
Automatic Recognition of Text Difficulty from Consumers Health Information
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
User-sensitive text summarization: application to the medical domain
User-sensitive text summarization: application to the medical domain
A Classifier to Evaluate Language Specificity of Medical Documents
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
A machine learning approach to reading level assessment
Computer Speech and Language
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
User Modelling for Personalized Question Answering
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Cognitively motivated features for readability assessment
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
An analysis of statistical models and features for reading difficulty prediction
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Real-time web text classification and analysis of reading difficulty
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Personalised news and scientific literature aggregation
Information Processing and Management: an International Journal
Personalization by website transformation: Theory and practice
Information Processing and Management: an International Journal
Learning a taxonomy from a set of text documents
Applied Soft Computing
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On the web, a huge variety of text collections contain knowledge in different expertise domains, such as technology or medicine. The texts are written for different uses and thus for people having different levels of expertise on the domain. Texts intended for professionals may not be understandable at all by a lay person, and texts for lay people may not contain all the detailed information needed by a professional. Many information retrieval applications, such as search engines, would offer better user experience if they were able to select the text sources that best fit the expertise level of the user. In this article, we propose a novel approach for assessing the difficulty level of a document: our method assesses difficulty for each user separately. The method enables, for instance, offering information in a personalised manner based on the user's knowledge of different domains. The method is based on the comparison of terms appearing in a document and terms known by the user. We present two ways to collect information about the terminology the user knows: by directly asking the users the difficulty of terms or, as a novel automatic approach, indirectly by analysing texts written by the users. We examine the applicability of the methodology with text documents in the medical domain. The results show that the method is able to distinguish between documents written for lay people and documents written for experts.