A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
ACM Transactions on Management Information Systems (TMIS)
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In this paper, we propose a model for evaluating the quality of general user-created documents. The model is based on supervised classification approach, in which output scores are considered as quality of given document. In order to utilize both textual and nontextual attributes of documents, we incorporated a number of objectively measurable, real-valued features selected upon predefined criteria for quality. Experiments on two datasets of real world documents show that textual features are stable indicators for evaluating documents' quality. Some features are inferred to be effective for general kinds of documents.