1. Detection of Implicit Citations for Sentiment Detection Awais Athar & Simone Teufel

2. Problem: Find ‘All’ Citations

3. Context-Enhanced Citation Sentiment Task 1: Find zones of influence of the citation – O'Conner 1982 (manual, partially implemented) – Kaplan et al (2009), for MDS – Related to implicit citation detection (Qazvinian & Radev, 2010) Task 2: Citation Classification – Many manual annotation schemes in Content Citation Analysis – Nanba and Okumura (1999) – Athar (2011)

4. Corpus Construction Starting point: Athar's 2011 citation sentence corpus Select top 20 papers; treat all incoming citations to these 1,741 citations (from >850 papers) 4-class scheme – objective/neutral – positive – negative – e cluded

5. Distribution of Classes

6. Task 1: Features for Classification S(i) or S(i-1) contains full formal citation (2 features) S(i) contains author name S(i) contains acronym associated with citation – METEOR, BLEU etc. S(i) contains a determiner followed by a “work noun” – This approach, These techniques

7. Task 1: Features (cont.) S(i) contains a “lexical hook” – The Xerox tagger (Cutting et al. 1992) … S(i) starts with a third person pronoun S(i) starts with a connector S(i), S(i+1) or S(i-1) starts with a subsection heading (3 features) S(i) contains other citations than one under review n-grams of length 1-3 (also acts as baseline)

8. Task 1: Methods and Results SVM 10-fold crossvalidation F-score

9. Task 2: Features for Classification n-grams of length 1 to 3 Dependency triplets (Athar, 2011) det_results_The nsubj_good_results cop_good_were det_results_The nsubj_good_results cop_good_were

10. Annotation Unit is the Citation Problem – There may be more than 1 sentiment /citation Annotation unit = citation. Projection needed: – For Gold Standard: assume last sentiment is what is really meant – For Automatic Treatment: merge citation context into one single sentence

11. Task 2: Methods and Results SVM 10-fold crossvalidation F-score

12. View of the Annotation Tool

13. Conclusion Detection of citation sentiment in context, not just citation sentence. New, large, context-aware citation corpus This gives us a new truth: – More sentiment recovered – Harder to determine Subtask of finding citation context: MicroF=.992; MacroF=.75 Overall result: MicroF=0.8; MacroF=0.68

14. Thank you! Questions?