1. Mining Anchor Text for Query Refinement
Reiner Kraft and Jason Zien
IBM Almaden Research Center
Mark Strohmaier

2. Problem Motivation 23% of search queries are single-term
Expanding the query can lead to more accurate searches
Previous studies indicate that anchor text is statistically similar to search queries
Can this similarity be exploited to improve search queries?

3. What is anchor text?
<a href=”this is the website”> This is the anchor text </a>
Destination pages can have multiple links pointing to them
Collections of anchor text can give a view of the destination page
Naïve approach:
Find links whose anchor text is similar to the query
Return the links destination pages to the user

4. Problems with naïve approach
High term frequency is not directly related to page quality
Repeated terms may lead to unnatural queries
IDF is not necessarily relevant
Anchor text may appear multiple times

5. Methods of Query Refinement
Weighting the number of occurrences
Weight based on the type of anchor text
Number of terms in the anchor text
Smaller terms is better
Number of characters in the anchor text
More concise queries are better

6. Benefits of the Anchor Text
There is much less anchor text than document text
Pages can have many incoming links
Refined anchor text can capture a degree of site popularity

7. Mining Anchor Text
Initial web crawl covered 33 million links on IBM intranet
Additionally, roughly 350,000 queries were analyzed
Both categories showed a similar relationship between length and number of occurrences

8. Pre-processing Summaries
Query refinement is sensitive to the number of terms
Too few may not lead to much improvement
Too many may lead to overspecialization
Best results were for MAXCOUNT = 3

9. Studies Performed Three different approaches were compared Anchor
Ranked Anchor Text refinement
Doc.SW
This ranked pages based on the most frequently occurring 2 and 3 term phrases
DOC
Similar to Doc.SW, but not counting stop words

10. Ranking Anchor Texts The results are ranked based on WCOUNT score
Number of terms in the anchor summary
Number of characters in the anchor summary

11. Comparison of Methods Second comparison tested 22 different queries
QUERYLOG processes and dynamically updates user queries based on previous ones, in a similar manner as ANCHOR

12. Conclusions
Using anchor text leads to better results than performing similar methods on document collections
A similar approach can be used to refine user search queries as well

13. Future Directions Broadening search queries
Lexical analysis, rather than straight textual
Pre- and Post- anchor text