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Identifying Sets of Related Words from the World Wide Web Thesis Defense 06/09/2005

Identifying Sets of Related Words from the World Wide Web Thesis Defense 06/09/2005. Pratheepan (Prath) Raveendranathan Advisor: Ted Pedersen. Outline. Introduction & Objective Methodology Experimental Results Conclusion Future Work Demo. Introduction.

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Identifying Sets of Related Words from the World Wide Web Thesis Defense 06/09/2005

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  1. Identifying Sets of RelatedWords from the World Wide Web Thesis Defense 06/09/2005 Pratheepan (Prath) Raveendranathan Advisor: Ted Pedersen

  2. Outline • Introduction & Objective • Methodology • Experimental Results • Conclusion • Future Work • Demo

  3. Introduction • The goal of my thesis research is to use the World Wide Web as a source of information to identify sets of words that are related in meaning. • Example, given two words - {gun,pistol} a possible set of related words would be {handgun, holster, shotgun, machine-gun, weapon,ammunition,bullet, magazine } • Example, given two words – {toyota, nissan, ford} A possible set of related words would {honda, gmc, chevy, mitsubishi}

  4. Examples Cont… • Example, given two words - {red,yellow} a possible set of related words would be { white,black,blue, colors, green} • Example, given two words - {George Bush,Bill Clinton} a possible set of related words would be { Ronald Reagan, Jimmy Carter, White House, Presidents, USA, etc }

  5. Application • Use sets of related words to classify Semantic Orientation of reviews. (Peter Turney) • Use sets of related words to find the sentiment associated with particular product. (Rajiv Vaidyanathan and Praveen Agarwal).

  6. Pros and Cons of using the Web • Pros • Huge amounts of text • Diverse text • Encyclopedia’s, Publications, Commercial Web Pages • Dynamic (ever-changing state) • Cons, • The Web creates a unique set of challenges, • Dynamic (ever-changing state) • News websites, Blogs • Presence of repetitive, noisy, or low-quality data. • HTML tags, web lingo (home page, information etc)

  7. Contributions • Developed an Algorithm that predicts sets of related words by using pattern matching techniques and frequency counts. • Developed an Algorithm that predicts sets of related words by using a relatedness measure. • Developed an Algorithm that predicts sets of related words by using a relatedness measure and an extension of the Log Likelihood score. • Applied sets of related words to problem of Sentiment Classification.

  8. Outline • Introduction & Objective • Methodology • Experimental Results • Conclusion • Future Work • Demo

  9. Interface to Web - Google • Reasons for using Google • Research is very much dependant on both the quantity and quality of the Web content. • Google has a very effective ranking algorithm called PageRank which attempts to give more important or higher quality web pages a higher ranking. • Google API – An interface which allows programmers to query more than 8 billion web pages using the Google search engine. (http://www.google.com/apis/).

  10. Problems with Google API • Restricted to 1000 queries a day • 10 Results for each query • No “near” operator (Proximity based search) • Maximum 1000 results. • Alternative • Yahoo API – 5000 Queries a day (Released very recently) • No “near” operator as well. • Cannot retrieve number of hits. Note: Google was used only as means of retrieving from the Information.

  11. Key Idea behind Algorithms • Words that are related in meaning often tend to occur together. • Example, A Springfield, MA , Chevrolet, Ford, Honda, Lexus, Mazda, Nissan, Saturn, Toyota automotive dealer with new and pre-owned vehicle sales and leasing

  12. Algorithm 1 • Features • Based on frequency • Takes only single words as input • Initial set 2 words • Frequency cutoff • Ranked by frequency • Smart stop list - • The, if, me, why, you etc (non-content words) • Web stop list • Web page, WWW, home,page, personal, url, information, link, text , decoration, verdana, script, javascript

  13. Algorithm 1 – High level Description • Create queries to Google based on the input terms. • Retrieve the top N number of web pages for each query. • Parse the retrieved web page content for each query. 3. Tokenize web page content into list of words and frequency. • Discard words that occur less than C number of times. 4. Find the common words between at least two of the sets of words. This set of intersecting words are the set of related words to the input term. 5. Repeat the process for I iterations by using the set of related words from the previous iteration as input.

  14. Algorithm 1 Trace 1 • Search Terms : S1={pistol, gun} • Frequency Cutoff – 15 • Num Results (Web Pages) – 10 • Iterations - 2

  15. Algorithm 1 –Step 1 • Create queries to Google based permutations of the Input Terms, • gun • gun AND pistol • pistol • pistol AND gun

  16. Algorithm 1 – Step 2 • Issue query to Google, • Retrieve the top 10 URLs for the query, • For each URL, retrieve the web page content, and parse the web page for more links. • Traverse these links and retrieve the content of those web pages as well. Repeat this process for each query.

  17. Trace 1 Cont… • Web pages for the query gun

  18. Trace 1 Cont… • Web pages for pistol

  19. Trace 1 Cont… • Web pages for gun AND pistol

  20. Trace 1 Cont… • Web pages for pistol AND gun

  21. Algorithm 1 – Step 3 3. Next, for the total web page content retrieved for each query, • Remove HTML Tags etc and retrieve text. • Remove stop words. • Tokenize the web page content into lists of words and frequency. Note: This would result in the following 4 sets of words, each set representing the words retrieved for each query.

  22. Words from Web pages after removing stop words

  23. Algorithm 1 – Step 4 4. Find the words that are common at least 2 sets. Let, • gun AND pistol • pistol AND gun • gun • pistol Related Set =

  24. Related Set 1 – Iteration 1

  25. Trace 1 Cont… Iteration 2 • 11 input terms – • Search terms created – • Rifle • Shooting • Guns • Cases • Airsoft • Shooting AND Guns • Guns AND Shooting • Guns AND Cases etc etc. Results in 112 = 121 queries to Google! Note: As you can see, the number of queries to Google increases drastically.

  26. Result Set 2 – {gun, pistol}

  27. Algorithm 1 – {red, yellow} Number of Results – 10 Frequency Cutoff - 15 Iterations - 1 Related Words

  28. Problems with Algorithm 1 • Frequency based ranking, • Number of input terms restricted to 2, • Input and output restricted to single words

  29. Algorithm 2 • Features • Based on frequency & relatedness score • Can takes input as single words or 2 word collocations • Relatedness measure based on Jiang and Conrath • Frequency cutoff and relatedness score cutoff • Ranked by score • Initial set can be more than 2 words • Bi-grams as output • Smart stop list • The, if, me, why, you etc • Web stop words + phrases • Web page, WWW, home page, personal, url, information, link, text , decoration, verdana, script, javascript

  30. Algorithm 2 – High level Description • Repeat same steps as in Algorithm 1 to retrieve initial set of related words (Add bigrams to results as well). • For each word returned by Algorithm 1 as a related word, • Calculate Relatedness of word to input terms. • Discard any word or bigram with a relatedness score greater than the score cutoff. • Sort remaining terms from most relevant to irrelevant. • Repeat Steps 1 – 2 for each iteration, using the set of words from iteration previous iteration as input.

  31. Relatedness Measure (Distance Measure) • Relatedness (Word1, Word2) = log (hits(Word1)) + log (hits(Word2)) – 2 * log (hits(Word1 Word2)) (Based on measure by Jiang and Conrath) • Example 1, hits(toyota) = 12,500,000 hits(ford) = 22,900,000 hits(toyota AND ford) = 50,000 = 32.41 • Example 2, hits(toyota) = 12,500,000 hits(ford) = 22,900,000 hits(toyota AND ford) = 150,000 = 30.82

  32. Relatedness Measure Cont… • Example 3, hits(toyota) = 1000 hits(ford) = 1000 hits(toyota AND ford) = 1000 Relatedness (toyota,ford) = 0 As the measure tends to approach zero, the relatedness between the two terms increase.

  33. Input Set – {gun, pistol}

  34. Algorithm 2 – {red, yellow} Number of Results – 10 Frequency Cutoff - 10 Score Cutoff - 30 Iterations - 1

  35. Problems with Algorithm 2 • Certain bigrams are not good collocations, • For example, {sunny, cloudy} Number of Results - 10 Frequency Cutoff - 15 Bigram Cutoff - 4 Score Cutoff - 30

  36. Algorithm 3 – High Level Description • Repeat same steps as in Algorithm 1 to retrieve initial set of related words (Add bigrams to results as well). • For each term returned by Algorithm 1 as a related word, • If the term is a bigram, • Validate if bigram is a valid collocation • If bigram is a valid collocation continue with step 2.2 else 2. Remove term from set of related words. • Calculate Relatedness of word to input terms. • Discard any word or collocation with a relatedness score greater than the score cutoff. • Sort remaining terms from most relevant to irrelevant.

  37. Verifying Bigrams • Adapt Log Likelihood (G2) Score to web hit counts • Example, “New York” • 4 Queries to Google “New *” “New York” “* York” “of the”

  38. Expected Values (621 * 3560) / 5670 (5049 * 3560) / 5670 (621 * 2110) / 5670 (5049 * 2110) / 5670

  39. Identifying a “bad” collocation • Bigram is discarded if, • Observed value for bigram is 0 (eg, “New York”) • Observed value for bigram is less than the expected value.

  40. Example Bigrams

  41. Methodology • Introduction & Objective • Methodology • Experimental Results & Evaluation • Conclusion • Future Work • Demo

  42. Evaluating Results • Compare with Google Sets • http://labs.google.com/sets • Human Subject Experiments • Around 20 people expanded 2-word sets to what they feel as a set of related words

  43. F-measure, Precision and Recall

  44. Comparison of Algorithm 1 & 2

  45. Algorithm 1 {jordan,chicago} Number of Results – 10 Frequency Cutoff - 15 Iterations - 1 Precision = 0, Recall = 0 F-measure = 0

  46. Algorithm 2 {toyota,ford, nissan} Number of Results – 10 Frequency Cutoff - 10 Score Cutoff - 30 Iterations - 1 Precision = 6/11 = 0.54, Recall = 6/11 = 0.54 F-measure = 0.54

  47. Algorithm 2 {january, february, may} Number of Results – 10 Frequency Cutoff - 10 Score Cutoff - 30 Iterations - 1 Precision = 9/9 = 1, Recall = 9/9 = 1 F-measure = 1

  48. Algorithm 2 {armani, versace} Number of Results – 10 Frequency Cutoff - 10 Bigram Cutoff - 4 Score Cutoff - 30 Iterations - 1 Precision = 11/20 = 0.55, Recall = 11/43 = .25 F-measure = 0.35 Not Entire Set

  49. Algorithm 2 {artificial intelligence, machine learning} Number of Results – 10 Frequency Cutoff - 10 Bigram Cutoff - 4 Score Cutoff - 32 Iterations - 1 Precision = 9/23 = 0.39, Recall = 9/48 = 0.1875 F-measure = 0.25

  50. Comparison of Algorithm 2 & 3 • {sunny, cloudy} Number of Results – 10 Frequency Cutoff - 10 Bigram Cutoff - 4 Score Cutoff - 30 Iterations - 1

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