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Identifying Intention Posts in Discussion Forums Meichun Hsu - PowerPoint PPT Presentation

Identifying Intention Posts in Discussion Forums Meichun Hsu Zhiyuan (Brett) Chen Malu Castellanos Bing Liu Riddhiman Ghosh What is Intention? Example Hello, I am going to buy a new high-end gaming laptop my budget is below than 1500$ ram


  1. Identifying Intention Posts in Discussion Forums Meichun Hsu Zhiyuan (Brett) Chen Malu Castellanos Bing Liu Riddhiman Ghosh

  2. What is Intention?

  3. Example Hello, I am going to buy a new high-end gaming laptop my budget is below than 1500$ ram should be more than 6gb,graphics card must be more than 2gb and the processor should be intel core i7 3rd generations or better.

  4. Explicit Intention I plan to buy this book. I am looking for a new car. I am going to travel to Atlanta.

  5. Implicit Intention Anyone knows the battery life of iPhone? What are the components in this laptop?

  6. Identifying Intention A totally NEW problem Explicit intention Applications like advertisement

  7. Problem Definition

  8. Two-Class Post Classification Transfer Learning: Use labeled data from other domains (source domains) to classify target domain

  9. The ways to express an intention are similar in different domains.

  10. Motivation Examples I want to buy a car. I want to buy a camera. I want to buy the tickets.

  11. Special Difficulties

  12. Noise I read many reviews of two Canon models which I'm considering for purchase, the Canon PowerShot S2 IS and the Canon PowerShot S3 IS. This is my second digital camera, my first being a Kodak EasyShare from about 4 years ago. …

  13. Imbalanced Shared Features

  14. EM Algorithm with NB (Nigam et al., 2000) E-step M-step Naïve Bayes

  15. Proposed Models FS-EM Co-Class

  16. FS-EM Incorporates feature selection into EM iterations Selects features from both labeled source (domain) data and unlabeled target (domain) data

  17. Co-Class Builds two classifiers based on FS-EM Solves the imbalanced shared feature problem

  18. Features & Feature Selection N-grams

  19. Evaluation

  20. Datasets 4 Domains from different forums (http://www.cs.uic.edu/~zchen/) Human annotation Cross-validation

  21. Evaluation Measures

  22. Supervised Learning (One Domain)

  23. Model Comparisons 3TR-1TE (Aue & Gamon, 2005) EM (Nigam, et al., 2000) ANB (Tan et al., 2009) FS-EM1 FS-EM2 Co-Class

  24. Model Comparisons

  25. Effects of Source Domains

  26. Conclusions Novel problem of identifying intention Suitable for transfer learning Two special difficulties Effectiveness of Co-Class

  27. Future Directions Sentence-level classification Extract intention components

  28. Q & A

  29. Dataset Download Link: http://www.cs.uic.edu/~zchen/

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