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Genre and Task for Web Page Filtering

Genre and Task for Web Page Filtering. Michael Shepherd Web Information Filtering Lab Faculty of Computer Science Dalhousie University. Research Team. Students Lei Dong Alistair Kennedy Richong Zhang Faculty Carolyn Watters Jack Duffy. Overview. Introduction Genre Task Summary.

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Genre and Task for Web Page Filtering

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  1. Genre and Task for Web Page Filtering Michael Shepherd Web Information Filtering Lab Faculty of Computer Science Dalhousie University

  2. Research Team • Students • Lei Dong • Alistair Kennedy • Richong Zhang • Faculty • Carolyn Watters • Jack Duffy

  3. Overview • Introduction • Genre • Task • Summary

  4. Introduction • The focus of our current research is the investigation of filtering techniques for the Web • This includes context-aware retrieval where context includes: • Adaptive user modeling • The user’s “task” • Information need • What it is the user is trying to do • We are moving to incorporate the notions of genre and task and to evaluate the impact that these have on filtering

  5. Filtering Task Genre User Profiles

  6. Motivation for Research • The Web has billions of documents • Average query is 2-3 words One document will satisfy our information need!

  7. But it’s more than just search • “Browsing or surfing the Web represents the main model for web use, especially among younger users.” (Hunter)

  8. Continuum Surfing Searching Browsing • Three general types [Marchionini] • Directed browsing – explicit info need • Semi-directed browsing – less well defined need • Undirected browsing - there is no real goal and the user is “surfing”

  9. Motivated Behaviour • Intrinsically Motivated Behaviour • “… is that which appears to be spontaneously initiated by the person in pursuit of no other goal than the activity itself.” [Enzle, Wright, Redondo] • “… engaging in a task for its enjoyment value…” [Deci, Ryan] • Extrinsically Motivated Behaviour • “… motivation is to engage in an activity as a means to an end … participation will result in desirable outcomes such as reward …” [Pintrich, Schunk]

  10. Task and Information Need Continuum General information gathering Explicit Information need So, one document may not satisfy the information need I’m shopping for a computer I want the price on the Dell Inspiron Notebook computer

  11. Why look at Genre and Task?

  12. Filtering Based on Adaptive User Profiles and IR-type of Task • Intrinsic Motivation • Fine-grained filtering of the Web is not feasible when the browsing task is “undirected” • Extrinsic Motivation • Fine-grained filtering of the Web is feasible when there is an explicit information need

  13. Genre • A genre is a “classifying statement” • It allows us to recognize items that are similar even in the midst of great diversity • Newspapers • Mystery novels • Office memos • socially recognized communicative purpose • Generally characterized by the tuple: <content, form>

  14. Cybergenre • Genre on the web • Characterized by the tuple <content, form, functionality> • Where functionality is the functionality afforded by the new medium, i.e., the web

  15. cybergenre extant novel spontaneous emergent replicated variant electronic newspaper multimedia newspaper personalized newspaper FAQ

  16. Recognizing Genres of Web Pages • The number of cybergenres is increasing, with different estimates putting the number at well over 1000 (depends on granularity) • It is difficult to know the boundaries of a genre and to know when one has crossed from one genre into another genre • It is difficult to know when a web page represents the emergence of a new genre

  17. Research Problems • How can we identify automatically the genre of a web page? • What features should be used in describing web pages? • How can we make this adaptive to recognize: • New genre when they emerge? • Genre classes that are fuzzy and genres that slide from one class to another?

  18. Research Questions • Can we identify home pages? • Can we distinguish among the sub-genres: • personal, corporate and organization home pages? • What influence does the functionality attribute have in distinguishing these genres and sub-genres?

  19. Machine Learning Model and Dataset • The dataset consisted of 321 web pages • 17 were classified manually as belonging to two of the three home page sub-genres • 94 corporate home pages • 93 personal home pages • 74 organization home pages • 77 noise pages • Neural Net Model • Single classifier with three target output classes • Three different classifiers, one for each of three target output classes

  20. Features • Content • Number of Meta tags used. • Does the page contain any phone numbers? • List of most common words appearing in between 16% and 40% of all documents. • Form • Number of images. • Does the page have its own domain, or is it in a sub-directory within a domain? • Size of file in bytes. • Number of words in the page. • Functionality • Number of Links in the Web Page. • Number of E-mail Links. • Prop. of links that are navigational links to other web pages within the same site. • Prop. of links that are links to locations within the same page. • Prop. of links that are links to other pages on other sites. • Number of form inputs • Is the first tag a Script tag?

  21. Terms Selected as Features

  22. Neural Net Categorization Target Categories Neural Net Personal Home Page Data Set of Web Pages of Known Genre Type Corporate Home Page Organization Home Page Input Feature Vector

  23. Evaluation • Recall • The proportion of web pages of genre type Gi that are correctly categorized into category Ci • Precision • The proportion of web pages categorized into category Ci that are of genre type Gi • F-measure(Gi) = the quality of the classifier with respect to web pages of genre type Gi

  24. 10-Fold Cross Validation • Used when data set is small in order to obtain statistically valid results

  25. Test Set 2 10 % Test Set 1 10 % Test Set 3 10 % Training Set 90%

  26. F-measures using separate classifiers with noise pages

  27. F-measures using single classifier with noise pages

  28. Misclassification tablesSingle Classifier <content, form, functionality>

  29. Genre Summary • We can recognize home pages from noise pages • We can distinguish personal home pages from corporate and organization home pages, but distinguishing between corporate and organizational home pages is difficult • Feature set needs a lot more attention paid to it

  30. Open Questions • What is an appropriate feature set? • Full evaluation of functionality attribute • What ML model to use? • Accuracy and scalability • Adaptive • Track recognized genres as they evolve • Recognize the introduction of a novel genre not seen previously • Is this like topic detection and tracking?

  31. Genre and Task on the Web? Roussinov, et al., Genre Based Navigation on the Web, HICSS’34

  32. Yahoo Directory

  33. Yahoo Directory • Yahoo categories are created and maintained manually • Creator of a web site submits a description • Editors review these • Can we automatically classify a web page by task?

  34. Experiment • Creation of data set • Data cleaning • 10-fold cross validation • Feature selection (IG) • Principal component analysis • Build Decision Tree • Testing

  35. Creation of Data Set • Selected 120 web pages randomly from Yahoo directories in each of: • Shopping • Health • Education • Selected 70 pages (NSHE) not from the Web that are not shopping, health or education • Total of 430 Web pages • Validated by 3 raters

  36. Data Cleaning • XML, HTML tags • <href>, <img>, <p> • Pictures, Audio files, Video files • Scripts • <javascript> • Stop words • Porter’s stemming algorithm

  37. Feature Selection Using the Information Gain (IG) • Employed as a term goodness criterion • Based on Information Theory • The number of “bits of information” gained by knowing the term is present or absent

  38. Information Gain (IG) • A measure of importance of the feature for predicting the presence of the class. The information gain of term t is defined to be denotes the set of categories in the target space.

  39. Information gain (IG) Number of documents in which term appears in each category

  40. Information gain (IG) 300 features

  41. Document Term Matrix 324 Documents (108 in each of Health, Shopping and Education) 300 terms as identified by the Information Gain measure

  42. Principal Component Analysis • Identifies patterns in data and is a way to express the data is such a way as to highlight their similarities and differences • Once these patterns have been found in the data, we can reduce the number of dimensions without much loss of data

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