1. Query Type Classification for Web Document Retrieval
In-Ho Kang, GilChang Kim
Presented by: Xiaoguang Qi

2. Introduction
Combine multiple evidence to compensate for the weakness of single evidence
Content-based & Link-based
Three types of user queries
Topic relevance task (informational)
E.g. “What is a prime factor?”
Homepage finding task (navigational)
E.g. “John Hopkins Medical Institutions”
Service finding task (transactional)
E.g. “Where can I buy concert ticket?”

3. Multiple Sources of Information
Content Information: tfidf
Link Information: PageRank
URL Information:
URLprior: (Kraaij et al) estimate a prior probability for a page of being an entry page based on its URL
Root (71.7%)
Subroot (13.2%) trec.nist.gov/pubs/
Path (5.7%) trec.nist.gov/pubs/trec9/papers/
File (5.7%)
Combination: linear combination

4. Topic Relevance Task & Homepage Finding Task
Text representation
Anchor text
Common content
Query term matching
and
sum
Combination of Information

5. Topic Relevance Task & Homepage Finding Task (Cont.)
Some conclusion
Topic relevance task
Homepage finding task
Text representation
Full text
Anchor text and title
Query term matching
“sum”
“and”
Combining URL and link information
useless
useful

6. User Query Classification
Preparation of language model
Distribution of query terms
Single-term query
Calculate chi-square
Multi-term query

7. User Query Classification (Cont.)
Mutual Information
Discriminate the two types of queries by using term co-occurrence frequency
Usage rate as an anchor text
If a query terms appears in titles and anchor texts frequently, the category of the given query is homepage finding task
Part-of-speech information
If a query has a verb except the “be” verb, we classify it into the topic relevance task

8. Experiments
Dataset: WT10g
Query classification

9. Experiments (Cont.)
The improvement of IR performance

10. Discussion & Conclusion
Identifying the queries of service finding tasks need more sophisticated analysis of anchor texts
User query classification can be applied to varies areas (e.g. MetaSearch)
Conclusion
Classify user queries into different categories
Use different strategies for different types of query
Experiments show improvement to IR system