I am presenting at The Seminario Interuniversitario de Investigación en Ciencias Matemáticas (Interuniversity Seminar on Mathematical Sciences Research, SIDIM).
This is one of the most important activities held in Puerto Rico for the promotion of Mathematics research. (http://sidim2009.uprr.pr/)
This year SIDIM will be held at University of Puerto Rico, Rio Piedras in March 6-7, 2009. The SIDIM program and book of abstracts is available at http://sidim.uprh.edu/libroSIDIM2009.pdf
I will be presenting new research work on IDF and a new model for the conditional specificity of terms. If you have followed previous posts on the topic of inverse document frequency, now you will understand why I have dissected the topic several times. Thank you all for your private comments and feedback on the topic.
My abstract follows:
Scaled Inverse Document Frequency: A Model for the Evaluation of the Conditional Specificity of Query Terms in Search Engine Collections
Edel Garcia, Internet Business Development Center, Interamerican University of Puerto Rico, Metropolitan Campus
Inverse document frequency (IDF) is a measure of the specificity of query terms over a collection of D number of documents that has been successfully incorporated into numerous vector space information retrieval models. Since these models assume term independence, the specificity of a given term, present in different queries, is assumed to be unique and independent from other query terms. To the best of our knowledge, there are no known models that condition the specificity of terms to the presence of other terms in a query.
This paper proposes a new measure called scaled inverse document frequency (SIDF) which evaluates the conditional specificity of query terms over a subset S of D and without making any assumption about term independence. S can be estimated from search results, OR searches, or computed from inverted index data. We have evaluated SIDF values from commercial search engines by submitting queries relevant to the financial investment domain. Results compare favorably across search engines and queries. Our approach has practical applications for `real-world’ scenarios like in Web Mining, Homeland Security, and keyword-driven marketing research scenarios. SIDF can be incorporated into a variety of information retrieval models as a global weight scoring system.
Keywords: inverse document frequency, conditional term specificity, web mining, search engines