1. A Simple Unsupervised Query Categorizer for Web Search Engines Prashant Ullegaddi and Vasudeva Varma Search and Information Extraction Lab Language Technologies Research Center IIIT-Hyderabad 500 032 ICON 2010

2. Outline Query categorization Related work Importance of ranking Challenges Design goals Our approach Results Conclusion

3. Query categorization (QC) Automatic categorization (classification) of user queries into one or more of pre-defined categories Note that categories are pre-defined and may vary across different applications However, for a particular application categories remain the same over a reasonable amount of time

4. Contributions Solving query categorization as a purely information retrieval problem Emphasis on importance of ranking of categories for QC systems Our system being simple and unsupervised in nature can establish a new baseline

5. Related Work Text categorization techniques (Shen et al., 2005, 2006): – Solve QC as a text categorization problem – But queries are not as rich as text documents in terms of context – Text classifiers are trained with a static vocabulary, which may not account for the dynamic nature of the Web.

6. …Related Work Graph based models (Diemert and Vandelle, 2009). – Constructing concept graphs built from search query logs – Once the concept graph is constructed, a query is categorized by traversing through the graph. – Not all search engines have the luxury of large search query logs.

7. Research Questions Can we solve QC by considering it purely as an IR problem? Can we combine the existing relatively standard IR techniques to solve QC? Can already categorized corpus be used for conducting query categorization? Can we establish a new baseline for QC systems?

8. Importance of ranking Consider category listings of two hypothetical systems for the query “Ipod” It is obvious from this example that ranking plays an important role for QC systems RankSystem (I) Category listing 1Entertainment/CelebritiesEntertainment/Music 2Computers/Hardware 3Computers/Software 4Info/References & Libraries 5Entertainment/MusicEntertainment/Celebrities

9. Challenges Category representation: – Categories need to be defined (covering most of the Web) – Each category needs to be represented by a set of documents that best describe that category. Category representation is needed in order to solve QC purely as an IR problem.

10. …Challenges Query expansion/enrichment: Usually queries are very short. Average query length in KDD Cup 2005 was 3.12 words. 22.5% of the queries were of length 3 words. 78.7% of the queries had at most 4 words.

11. Category Representation Categories of Open Directory Project (ODP) for QC Web documents that are classified under a category represent that category. Approximately 2.4 million English documents (of ODP) to represent categories These documents are classified into approximately 380K categories. Here the assumption is that these categories cover the entire Web. This corpus of ODP documents is used to perform QC.

12. Design Goals Our design goals: – Simple – Unsupervised framework – Implementable on Web scale – To solve QC as a “search” problem since “search” is a task a Web search can afford for free.

13. Our Approach Query expansion module ODP documents retrieval Query categorization Taxonomy mapping (ODP to target space) (Optional) Expanded Query ODP documents ODP Categories Target Categories

14. Query Expansion Pseudo relevance feedback query expansion Submit query to a Web search engine Collect stemmed terms (Q’) from title and snippets for top N search results Stop word removal Weight on document frequency (DF) measure Query Web Search Engine Web documents Stemmed terms

15. …Query Expansion Common concepts for a query usually occur in most of the top web documents obtained for a query This information is best captured by DF These common concepts represent the query “Serena Williams” Web Search Engine ……………..…Tennis..sports….. ………WTA ……………..…Tennis..sports….. ………WTA ………..........Tennis ………………… Wimbledon. ………..........Tennis ………………… Wimbledon. ……tennis… ……………… …..sports…....…..WTA.. ……tennis… ……………… …..sports…....…..WTA.. Tennis Sports WTA Wimbl edon Tennis Sports WTA Wimbl edon

16. Central Idea The ODP documents that match the query- related concepts are good enough to carry out QC In essence, topically similar documents This fact is leveraged in our unsupervised approach to QC

17. Query Categorization Search the expanded query on the ODP Web document corpus ODP documents retrieved for the query belong to at least one ODP category; resulting in query categorization An optional taxonomy mapping in case target categories are different from that of ODP

18. Taxonomy Mapping for KDD Cup dataset We map ODP categories to KDD cup categories to evaluate on KDD Dataset Note that computation of these mappings is one time and offline

19. …Taxonomy Mapping Target category t Stem words in t Search over the ODP category descriptions Search Category Descriptions C -- retrieved ODP categories Mappings: t to every category in C Reverse: Every ODP category in C can be mapped to t Mappings Store these mappings for target category t. Repeat for other target categories

20. …Taxonomy Mapping Search the target categories in the category ODP descriptions For a target category t, let the set of retrieved ODP categories be C Map every category in C to target category t. Repeat this for other target categories, and obtain mappings

21. …Taxonomy Mapping Let C (Q) be the set of ODP categories returned for a query Q The categories in target space to which most of the categories of C (Q) are getting mapped to will be ranked higher Top K categories in target space are returned as top K target categories for the query

22. Dataset KDD Cup 2005 dataset (Lie et al., 2005) A set of unlabeled 800K queries sampled MSN search query logs 67 predefined categories A set of 800 queries (sampled from the 800K queries) was labeled Three labelers independently labeled this set Each query was tagged with at most 5 categories This dataset serves as the standard dataset for QC evaluation

23. Evaluation Metrics Precision, Recall and F1 are defined, respectively, as follows:

24. Results ApproachPrecisionF1Prec@k State of the art (Shen et al., 2005)0.4140.4440.599 Best today (Shen et al., 2006b)0.4650.461NA KBS (Diemert and Vandelle 2009)0.6140.460NA Our System0.4280.4150.624 (+4.2%) *High precision reported by KBS System is due to binary categorization

25. On Results Though F1 reported for our system is marginally lower, we believe our system should be viewed from a different perspective Solve QC purely as an information retrieval problem Combined relatively standard techniques to solve QC making it – simple, and – implementable on a very large scale

26. ….On Results Our system is unsupervised in nature Our system does not make use of resources like search query logs Thus, we believe the results reported complement our design goals to a reasonable extent

27. Conclusion A simple, unsupervised yet effective approach to query categorization Leverages already categorized corpus (ODP) to perform QC Advantages – Simple approach – Unsupervised – Existing IR techniques can be used – Avoids Multiclass classification