detection of implicit citations for sentiment detection

Detection of Implicit Citations for Sentiment Detection

Awais Athar & Simone Teufel

Problem: Find ‘All’ Citations

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)

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

x

Distribution of Classes

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

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)

Task 1: Methods and Results

• SVM• 10-fold crossvalidation• F-score

Task 2: Features for Classification

• n-grams of length 1 to 3• Dependency triplets (Athar, 2011)

det_results_Thensubj_good_resultscop_good_were

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

Task 2: Methods and Results

• SVM• 10-fold crossvalidation• F-score

View of the Annotation Tool

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

Thank you!

Questions?