Rutgers' HARD Track Experiences at TREC 2004

The goal of our work in the HARD track was to test techniques for using knowledge about various aspects of the information seeker’s context to improve IR system performance. We were particularly concerned with such knowledge which could be gained through implicit sources of evidence, rather than explicit questioning of the information seeker. We therefore did not submit any clarification form 1 , preferring to rely on the categories of supplied metadata concerning the user which we believed could, at least in principle, be inferred from user behavior, either in the past or during the current information seeking episode. The experimental condition of the HARD track was for each site to submit at least one baseline run for the set of 50 topics, using only the title and (optionally) description fields for query construction. The results of the baseline run(s) were compared with the results from one or more experimental runs, which made use of the supplied searcher metadata, and of a clarification form submitted to the searcher, asking for whatever information each site thought would be useful in improving search results. We used only the supplied metadata, for the reasons stated above, and especially because we were interested in how to make initial queries better, rather than in how to conduct a dialogue with a searcher. There were five categories of searcher metadata for each topic (not all topics had values for all five): Genre, Familiarity, Geography, Granularity and Related text(s), which were intended to represent aspects of the searcher’s context which might be useful in tailoring retrieval to the individual, and the individual situation. We made the assumption that at least some of these categories would be available to the IR system prior to (or in conjunction with) the specific search session, either through explicit or implicit evidence. Therefore, for us the HARD track experimental condition was designed to test whether knowledge of these contextual characteristics, and our specific ways of using that knowledge, would result in better retrieval performance than a good IR system without such knowledge. We understood that there would be, in general, two ways in which to take account of the metadata. One would be to modify the initial query from the (presumed) searcher, before submitting it for search; the other would be to search with the initial query, and then to modify (i.e. re-rank) the results before showing them to the searcher. We used both, but mainly concentrated on the latter of these techniques in taking account of the different types of metadata. 2 Hypotheses for How to Take Account of Metadata Categories and Values Our general approach was to generate hypotheses about how to take account of each of the categories of metadata information in order to improve retrieval effectiveness, to operationalize them in the TREC HARD setting, to test each hypothesis individually on the training corpus, and then to combine the best-performing ones from each category to generate a final result list for the test corpus. Below are summarized the various improvement methods used based on metadata: Table 1. Methods for improving retrieval effectiveness based on metadata.