Section Based Relevance Feedback

1. Section Based Relevance Feedback Student: Nat Young Supervisor: Prof. Mark Sanderson

2. Relevance Feedback • SE user marks document(s) as relevant • E.g. “find more like this” • Terms are extracted from full document • Whole document may not be relevant • Could marking a sub-section relevant be better?

3. Test Collections • Simulate a real user’s search process • Submit queries in batch mode • Evaluate the result sets • Relevance Judgments • QREL: <topicId, docId> pairs (1 … n) • Traditionally produced by human assessors

4. Building a Test Collection • Documents • 1,388,939 research papers • Stop words removed • Porter Stemmer applied • Topics • 100 random documents • Their sub-sections (6 per document)

5. Building a Test Collection • In-edges • Documents that cite paper X • Found 943 using the CiteSeerX database • Out-edges • Documents cited by paper X • Found 397 using pattern matching on titles

6. QRELs • Total • 1,340 QRELs • Avg. 13.4 QRELs per document • Previous work: • Anna Richie et. al. (2006) • 82 Topics, Avg. 11.4 QRELs • 196 Topics, Avg. 4.5 QRELs • Last year • 71 Topics, Avg. 2.9 QRELs

7. Section Queries • RQ1 Do the sections return different results?

8. Section Queries • RQ2 Do the sections return different relevant results? Avg. = The average number of relevant results returned @ 20. E.g. Abstract queries returned 2 QRELs

9. Section Queries Average intersection sizes of relevant results E.g. Avg(|Abstract ∩ All|) = 0.63 Avg(|Abstract \ All|) = 1.37 100 - ((0.63 / 2) * 100) = 68.5% difference

10. Section Queries Average set complement % of relevant results E.g. Section X returned n% different relevant results than section Y

11. Next • Practical Significance • Does SRF provide benefits over standard RF?