Mehran Sahami

1. A Web­based Kernel Function for Measuring the Similarity of Short Text Snippets Timothy D. Heilman Mehran Sahami

2. Introduction • Wish to determine how similar two short text snippets are. • High degree of semantic similarity • United Nations Secretary General vs Kofi Annan • AI vs Articial Intelligence • Share terms • graphical models vs graphical interface 5%

3. Related Work • Query expansion techniques • Other means of determining query similarity • Set overlap (intersection) • SVM for text classification • Latent Semantic Kernels (LSK) • Semantic Proximity Matrix • Cross-lingual techniques 10%

4. A New Similarity Function • represent a short text snippet (query) to a search engine S • be the set of n retrieved documents • Compute the TFIDF term vector for each document • Truncate each vector to include its m highest weighted term 15%

5. Normalize • Let be the centroid of the L2 normalized vector • Let QE(x) be the L2 normalization of the centroid C(x) 20%

6. Kernel Function 25%

7. Initial Results with Kernel • Three genres of text snippet matching • Acronyms • Individuals and their positions • Multi-faceted terms 30%

8. Acronyms 35%

9. Individuals and their positions 40%

10. Multi-faceted terms 45%

11. Related Query Suggestion • Kernel function for • u is any newly issued user query • A repository Q of approximately 116 million popular user queries issued in 2003, determined by sampling anonymized web search logs from the Google search engine 50%

12. Algorithm • Given user query and list of matched queries from repository • Output list of queries to suggest • Initialize suggestion list • Sort kernel scores in descending order to produce an ordered list of corresponding queries • MAX is set to the maximum number of suggestions 55%

13. Post-Filter |q| denotes the number of terms in query q 60%

14. Evaluation of Query Suggestion System • suggestion is totally off topic. • suggestion is not as good as original query. • suggestion is basically same as original query. • suggestion is potentially better than original query. • suggestion is fantastic - should suggest this query since it might help a user find what they're looking for if they issued it instead of the original query. 65%

15. Evaluations 70%

16. Average ratings at various kernel thresholds 75%

17. Average ratings versus average number of query suggestions 80%

18. Application in QA • K("Who shot Abraham Lincoln", "John Wilkes Booth") = 0.730 • K("Who shot Abraham Lincoln", "Abraham Lincoln") = 0.597 85%

19. Conclusion • A new kernel function for measuring the semantic similarity between pairs of short text snippets • The first is improvement in the generation of query expansions with the goal of improving the match score for the kernel function

20. Term Weighting Scheme • The weight associated with the term in document is defined to be : • Where is the frequency of in • N is the total number of ducuments , and is the total number of documents that contain

21. Lp Norm • Given by: • Most common cases • P=1 ,This is the L1 norm, which is also called Manhattan distance • P=2 ,This is the L2 norm, which is also called the Euclidean distance • P= , This is the L norm, also called the infinity norm or the Chebyshev norm