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“Is the Sky Pure Today?” AwkChecker: An Assistive Tool for Detecting and

“Is the Sky Pure Today?” AwkChecker: An Assistive Tool for Detecting and Correcting Collocation Errors. Taehyun Park, Edward Lank, Pascal Poupart, Michael Terry David R. Cheriton School of Computer Science University of Waterloo, Waterloo, ON, Canada, N2L 3G1. ACM UIST 2008. Motivation.

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“Is the Sky Pure Today?” AwkChecker: An Assistive Tool for Detecting and

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  1. “Is the Sky Pure Today?” AwkChecker: An Assistive Tool for Detecting and Correcting Collocation Errors Taehyun Park, Edward Lank, Pascal Poupart, Michael Terry David R. Cheriton School of Computer Science University of Waterloo, Waterloo, ON, Canada, N2L 3G1 ACM UIST 2008

  2. Motivation • writing aids for non-native speakers • non-native speakers can learn a foreign • language's rules for spelling and grammar, • but not easy to learn word pairs. Ex. take their shoes down vs take their shoes off more common expression

  3. AwkChecker • detect collocation errors and suggest alternatives

  4. Contributions • Define collocation errors as a function of the • relative frequency of phrase usage within a • corpus. • Presents algorithms for suggesting alternatives • based on the specific types of errors made by • NNSs. 1. Insertion (I went to home I went home) 2. Deletion (I am student I am a student ) 3. Transposition (he’s talking with his full mouth he’s talking with his mouth full) 4. Substitution (pure sky clear sky)

  5. Detecting Collocation Errors • acceptability of a phrase e g(e): frequency of input phrase g(c): frequency of alternative phrase f (e,c): edit distance between e and c If A(e) is less than a user-customizable threshold, the phrase e is flagged as a collocation error.

  6. Evaluation - User Testing • five non-native speakers • had never seen tools such a system before • positive reactions • employed AwkChecker to check articles and • prepositions pass judgment (to/on) <noun>

  7. Automatic Collocation Suggestion in Academic Writing Jian-Cheng Wu1 Yu-Chia Chang1,* Teruko Mitamura2 Jason S. Chang1 1 National Tsing Hua University Hsinchu, Taiwan 2 Carnegie Mellon University Pittsburgh, United States ACM ACL 2010

  8. Goals • automate suggestions forverb-noun lexical collocation • Verb-noun collocations are recognized as presenting the most • challenge to students (Howarth, 1996; Liu,2002). • word choice of verbs in collocations which are considered as • the most difficult ones for learners to master (Liu,2002; Chang, • 2008).

  9. Collocation Inspector

  10. Algorithm of Producing Suggestions

  11. Collocation Extraction Ex. We introduce a novel method for learning to find documents on the web. We proposed that the web-based model would be more effective than corpus-based one. • Use dependency parser (Stanford Parser) dobj (introduce-2, method-4)

  12. Using a Classifier for the Suggestion task

  13. EffectiveFeature Selection • Training algorithm: Maximum Entropy - Use contextual features(head , ngram) Ex: Weintroducea novel methodfor learning to find documents on the web.

  14. Example • Input : • There are many investigations about wireless network communication, especially it is important to add Internet transfer calculation speeds. Result

  15. Experiment • Training Corpus: CiteSeer (20,306 abstracts, 95,650 sentences) • 790 verbal collocates are identified as tagged classes • Test data: randomly select 600 sentences not overlapping • with the training set.

  16. The YouTube Video Recommendation System James Davidson、Benjamin Liebald 、Junning Liu Taylor Van Vleet 、 Palash Nandy Google Inc ACM RecSys 2010

  17. Personalized recommendations • user’s previous activity on the site

  18. Goals • Help users find high quality videos relevant to • their interests. • Recommendations should be updated regularly • and reflect a user’s recent activity on the site. • Maintain user privacy.

  19. Challenges • Videos as they are uploaded by users often • have no or very poor metadata (title, description). • Videos on YouTube are mostly short form (under • 10 minutes in length) • Many of the interesting videos on YouTube have • a short life cycle.

  20. System Design 1 seed 2 Top-N candidates … Videos rank using relevance and diversity. user

  21. Input Data(seed) • videos that were watched • (potentially beyond a certain threshold) • videos that were explicitly favorited, “liked”, • rated or added to playlists

  22. Related Videos(candidates) • relatedness score : total occurrence counts across all sessions for videos vi and vj : global popularity for videos vi and vj Threshold :overall view count Top-N candidates of vi

  23. Generating Recommendation Candidates Candidate set: S: seed set R: related videos n: distance of n from any video in the seed set

  24. Ranking • video quality • (view count,commenting, sharing activity…) • user specificity • (consider properties of the seed video) • diversification • (videos that are too similar to each other are removed)

  25. Evaluation

  26. Text Cohesion Visualizer Chakarida Nukoolkit, Praewphan Chansripiboon Pornchai Mongkolnam, Richard Watson Todd* Computer Science Program, School of Information Technology School of Liberal Arts* King Mongkut’s University of Technology Thonburi Bangkok, 10140 Thailand IEEE ICCSE 2011

  27. Goals • design of a prototype system developed to help • analyze the lexical coherence of essays • provide visualized output as writing feedback to • users

  28. System Flowchart Preprocessing (Stanford Part Of Speech tagger) Matching keywords Creating bond table

  29. Matching keywords • count the number of matched words (link) between • any two sentences • four types of matching: • 1. repetition • 2. complex repetition • 3. paraphrase(synonyms,hypernyms) • 4. pronoun

  30. Creating bond table • indicating whether or not there is a bond between • sentences.

  31. six types

  32. Conclusion • We proposed an application that can detect the cohesion errors in text • correctly as experts indicate. • The system’s accuracy is at an acceptable level according to expert • opinion. • In future work, we first plan to improve the process of matching keywords • for more accurate results by augmenting the existing process with more • specific linguistic rules.

  33. See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries Chao Zhang、Lidan Shou、 Ke Chen、Gang Chen College of Computer Science Zhejiang University, China ACM SIGIR 2012

  34. Spatio-Visual Keyword • searches for introductory information about a distant grand church • within her eyesight.

  35. Goals visually conspicuous semantically relevant physicalspace document space WYRIWYS (What-You-Retrieve-Is-What-You-See)

  36. Motivation • state-of-the-art spatial retrieval methods are mostlydistance-based • butoverlook the visibility of objects. Italian food

  37. System Flowchart Ranking Mechanism Visibility Analysis

  38. Experiment • Data set: • 1.street objects in Los Angeles (contains 131,461 MBRs) • 2.Gowalla (consists of 28,867 Web documents)

  39. 柏安 亞婷 家愷 冠中 ???

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