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BoilerPlate Detection using Shallow Text Features

BoilerPlate Detection using Shallow Text Features. Christian Kohlschütter, Peter Fankhauser, Wolfgang Nejdl. Classification . What is classification? Goal: previously unseen records should be assigned a class as accurately as possible. Training Set

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BoilerPlate Detection using Shallow Text Features

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  1. BoilerPlate Detection using Shallow Text Features Christian Kohlschütter, Peter Fankhauser, Wolfgang Nejdl

  2. Classification • What is classification? • Goal: previously unseen records should be assigned a class as accurately as possible. • Training Set • Each record contains a set of attributes, one of the attributes is the class. • A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

  3. Applications Of Classification • Fraud Detection • Customer Attrition/Churn • Spam Mail • Direct marketing • Many more….

  4. Text Classification • Text Classification is the task of assigning documents expressed in natural language into one or more classes belonging to a predefined set. • The classifier: • Input: a document x • Output: a predicted class yfrom some fixed set of labels y1,...,yK

  5. Application: Text Classification • Classify news stories as World, US, Business, SciTech, Sports, Entertainment, Health, Other • Classify student essays as A,B,C,D, or F. • Classify pdf files as ResearchPaper, Other • Boilerplate Detection in web pages • Text not related to main content

  6. Web Page

  7. Boilerplate Content

  8. BoilerPalte Content Removed

  9. Web Page Features Used for Classification • Strutural Features • HTML Tags • Shallow Text Features • Average word/sentence length • Densitometric Features • Text Density

  10. Shallow Text Features •Examine Document at Text Block Level • Numbers: Words, Tokens contained in block • Average Lengths: Tokens, Sentences • Ratios: Uppercased words • Block-level HTML tags <P>, <Hn>, <DIV> • Densities: Link Density (Anchor Text Percentage) Link Density = No.of Tokens within A TagNo.of Token in the block

  11. Token Density ρ(b) = No.of tokens in block wrapped lines in block • Wrap text at a fixed line width (e.g. 80 chars)

  12. Classification Method Used • 2 Class(Boilerplate vs main content) problem • 4 Class(Boilerplate , main content, headline, supplemental) problem • Weka is used to examine the per-feature information gain and evaluate machine-learning classifiers based on DecisionTrees (1R and C4.8) • Weka is a collection of machine learning algorithms for data mining tasks

  13. Learn One Rule -1R • The objective of this function is to extract the best rule that covers the current set of training instances • What is the strategy used for rule growing ? • What is the evaluation criteria used for rule growing ? • What is the stopping criteria for rule growing ?

  14. Learn One Rule: Rule Growing Strategy • General-to-specific approach • It is initially assumed that the best rule is the empty rule, r : { } → y, where y is the majority class of the instances • Iteratively add new conjuncts to the LHS of the rule until the stopping criterion is met • Specific-to-general approach • A positive instance is chosen as the initial seed for a rule • The function keeps refining this rule by generalizing the conjuncts until the stopping criterion is met

  15. Rule Evaluation and Stopping Criteria • Evaluate rules using rule evaluation metric • Accuracy= (TP+TN)/(TP+FP+FN+TN) • Coverage • F measure : measure of test accuracy considering both precision and recall • A typical condition for terminating the rule growing process is to compare the evaluation metric of the previous candidate rule to the newly grown rule. • 1R used: • Block with a text density less than 10.5 is regarded boilerplate

  16. Data Set • GoogleNews Dataset • 621 news articles from 408 web sites, randomly sampled from a 254,000 pages crawl of English Google News over 4 months, • manually assessed by L3S research group

  17. Cost-Sensitive Measures • Precision P = TP TP+FP • Recall R= TP TP+ FN • F-measure F= 2RP R+P

  18. Linguistic Interpretation • Descriptive nature of long text • Short text – Grammatically incomplete • Eg. “Contact us”,” Read more”

  19. Inference from Experiments • Combination of just two features(num of words and link density) leads to a simple clssification model • Very high Classification/Extraction Accuracy (92-98%)

  20. Impact of Boilerplate detection to search • Keywords that are not relevant to the actual main content can be avoided • Increase of Retrieval Precision • Experimented on BLOGS06 Web research collection • For 50 top-k searches

  21. QUESTIONS ???

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