1. Scalable Methods for Estimating Document Frequencies of Collocations in Databases Tan Yee Fan 2006 December 15 WING Group Meeting

2. Motivation Input:  A list of items, L Output:  For each a, b in L, the cooccurrence value between a and b Often done by querying some database for document frequencies  e.g. f(a), f(b) and f(a  b) Many cooccurrence measures need f(a  b)

3. Problem Statement Input  A list of items, L Output  For each a, b in L, the document frequency f(a  b) in database Naïve pairwise algorithm need O(n 2 ) queries  Not scalable (e.g. n ~ 1000)  Bandwidth issues and server overload

4. Related Work C-PANKOW (Cimiano et al., 2005)  Matching named entities to concepts POLYPHONET (Matsuo et al., 2006)  Building a social network Avoid pairwise queries as far as possible  Both C-PANKOW and POLYPHONET perform “document processing” to achieve this goal  Is document processing really necessary?

5. Related Work QProber (Ipeirotis, 2002)  Obtain a sample of documents from database  Select some words to query and fit a power law curve  Estimate document frequencies of the rest Figure from Ipeirotis (2002)

6. This Project Extend QProber algorithm to collocations Algorithm  Obtain a sample of documents from database  Select some collocations to query and fit a power law curve  Estimate document frequencies of the rest

7. Query Selection Strategy Query selection strategy  For each word w, order collocations in sampled documents containing w by rank  Uniformly select q collocations to query Use O(qn) queries, with q << n decreasing rank

8. Experiment Database of 2000 newsgroup articles Evaluated on a lexicon of 100 words Vary sample size s and number of queries q

9. Conclusion Possible to estimate document frequencies of collocations reliably using O(n) queries Next step  Can the methods be applied to disambiguating author names, publication venue titles, etc.?

10. Additional Slides

11. Estimating Actual Document Frequencies Alternative method  For each word w, fit a power law curve using the collocations containing w  Estimation for unknown collocation w 1  w 2 : Average the values estimated from the curve of w 1  and the curve of w 2 Problem  Quality of each curve is not as good as lesser training examples used

12. Query Selection Strategy Alternative strategy  Uniform selection of collocations to query without regard to frequencies Problem  Together with alternative method, can produce large errors due to selection of collocations at the tail of the power law curve to query