1. UROP Research Update Citation Function Classification Eric Yulianto A0069442B 22 February 2013

2. Outline Motivation Problem Related Work Current Progress Follow Up

3. Motivation To assist researchers during paper review process. Quick categorization with minimal amount of reading. Help prioritize more important papers.

4. Problem Given a citation on a paper.  What is the purpose of the citation? Need to repeatedly read a section of the paper.  Intention may not be obvious from the citation sentence.

5. Example Excerpt from (Busemann, Schmeier, & Arens, 2000)  SVMs are described in (Vapnik, 1995). SVMs are binary learners in that they distinguish positive and negative examples for each class. (neutral context)  In all experiments the SVM Light system outperformed other learning algorithms, which confirms Yang’s (Yang and Liu, 1999) results for SVM's fed with Reuters data. (positive context)

6. Related Work Teufel et al., 2006  Feature used: Cue phrases Verb Clusters Verb Tense Modality Self-citation indicator  Ibk/k-Nearest Neighbour Algorithm  Accuracy: 77%

7. Related Work Angrosh et al., 2010  Citation classification => Sentence classification  Related Work Section only.  Feature Used: Word Category. Presence of citation in previous sentence.  Conditional random field.  Generally perform well: Accuracy: 96.51%.  Did not perform well on citation sentence.

8. Related Work Dong and Schafer, 2011  Feature used: Cue words. Physical: Location,Popularity,Density,AvgDens. Sentence syntax  Ensemble-style self-training algorithm.

9. Current Progress (Analysis) Citation scheme  Adopt and modify the scheme done in Teufel et al., 2006.  12 classes => 4 classes. Weakness CompareContrast Positive Neutral

10. Current Progress (Analysis) Dataset  ANLP Conference from ACL Anthology.  Context extracted from ParsCit output.  Distribution: 609 citations Weakness:30 CompareContrast:72 Positive:236 Neutral:271

11. Current Progress (Analysis) Classification Algorithm  Weka Implementation of Naive Bayes and SVM  Uses chi-square attribute selection filter

12. Current Progress (Analysis) Feature Used and Tested:  Cue Words  Cue Words + chi-square filter  Word Categories (Angrosh et al., 2010)

13. Current Progress (Analysis) Feature Used NaiveBaye s SVM Cue Words 64.37%67.16% Cue Words + filter 66.48%68.95% Angrosh reimplementation 51.24%49.90%

14. Ongoing Process Feature extracted but not yet tested:  Physical Features (Dong and Schafer, 2011) Location Density Popularity  Author and Title Information  Publication Year

15. Follow Up Add more features that can help differentiate the citation functions. Larger dataset Split the classification into two stages: – Use the metadata(physical features, author information, title information, publication year) – Use the cue words to refine the classification

16. Thank You