a comparison of techniques for estimating idf values to generate lexical signatures for the web
For bounded datasets such as the TREC Web Track the computation of term frequency (TF) and inverse document frequency (IDF) is not difficult. However, since IDF cannot be directly calculated for the entire web, it must be estimated. We see a need to estimate accurate IDF values to generate TF-IDF based lexical signatures (LSs) of web pages. Future applications for generating such LSs require a real time IDF computation. Therefore we conducted a comparison study of different methods to estimate IDF values of web pages. Our objective is to investigate how accurate these estimation methods are compared to the a baseline. We use the Google N-grams as our baseline and compare it against two IDF estimation techniques which are based on: 1) a "local universe" consisting of textual content and the according document frequencies from copies of URLs from the Internet Archive and 2) "screen scraping", a technique to query the Google web interface for document frequencies. We found a term overlap of 70 to 80\% between the results of the two methods and the baseline. We further discovered a great agreement in rank correlation of TF-IDF ranked terms between our methods. Kendall ? is approximately 0.8 and the M-Score (penalizing discordances in higher ranks) is even higher, it peaks at well above 0.9. These preliminary results lead us to the conclusion that both methods are appropriate for creating accurate IDF values for web pages.
lexical signature inverse document frequency