Parameter Tuning in Pivoted Normalization for XML Retrieval: ISI@INEX09 Adhoc Focused Task

Abstract

This paper describes the work that we did at Indian Statistical Institute towards XML retrieval for INEX 2009. Since there has been an abrupt quantum jump in the INEX corpus size (from 4.6 GB with 659,388 articles to 50.7 GB with 2,666,190 articles), retrieval algorithms and systems were put to a ‘stress test’ in the INEX 2009 campaign. We tuned our text retrieval system (SMART) based on the Vector Space Model (VSM) that we have been using since INEX 2006. We submitted two runs for the adhoc focused task. Both the runs used VSM-based document-level retrieval with blind feedback: an initial run (indsta_VSMpart) used only a small fraction of INEX 2009 corpus; the other used the full corpus (indsta_VSMfb). We considered Content-Only (CO) retrieval, using the Title and Description fields of the INEX 2009 adhoc queries (2009001-2009115). Our official runs, however, used incorrect topic numbers. This led to very dismal performance. Post-submission, the corrected version of both baseline and with-feedback document-level runs achieved competitive scores. We performed a set of experiments to tune our pivoted normalization-based term-weighting scheme for XML retrieval. The scores of our best document-level runs, both with and without blind feedback, seemed to substantially improve after tuning of normalization parameters. We also ran element-level retrieval on a subset of the document-level runs; the new parameter settings seemed to yield competitive results in this case as well. On the evaluation front, we observed an anomaly in the implementation of the evaluation-scripts while interpolated precision is being calculated. We raise the issue since a XML retrievable unit (passage/element) can be partially relevant containing a portion of non-relevant text, unlike document retrieval paradigm where a document is considered either completely relevant or completely non-relevant.