Automated Detection of Health Websites' HONcode Conformity: Can N-gram Tokenization Replace Stemming?
Authors evaluated supervised automatic classification algorithms for determination of health related web-page compliance with individual HONcode criteria of conduct using varying length character n-gram vectors to represent healthcare web page documents. The training/testing collection comprised web page fragments extracted by HONcode experts during the manual certification process. The authors compared automated classification performance of n-gram tokenization to the automated classification performance of document words and Porter-stemmed document words using a Naive Bayes classifier and DF (document frequency) dimensionality reduction metrics. The study attempted to determine whether the automated, language-independent approach might safely replace word-based classification. Using 5-grams as document features, authors also compared the baseline DF reduction function to Chi-square and Z-score dimensionality reductions. Overall study results indicate that n-gram tokenization provided a potentially viable alternative to document word stemming.
Health on The Net Foundation, Geneva, Switzerland.
Célia
C
Boyer
Health on The Net Foundation, Geneva, Switzerland.
Ljiljana
L
Dolamic
Université Lille 3, France.
Natalia
N
Grabar
D054626:Consumer Health Information / Q000592:standards* / Q000706:statistics & numerical data*; D005240:Feasibility Studies; D019983:Guideline Adherence / Q000592:standards / Q000706:statistics & numerical data*; D038622:Internationality; D020407:Internet / Q000592:standards* / Q000706:statistics & numerical data; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D017410:Practice Guidelines as Topic; D000069553:Supervised Machine Learning