Retrieval Performance and Visual Dispersion of Query Sets.

In the course of eight TREC Conferences, retrieval performance of all systems started high and then declined. This was especially true for conference 5. Only in conferences 7 and 8 have performance levels reached those initially achieved. In this paper, scaling of the corpus of 450 TREC topics is performed. It is observed that as the visual dispersion of a topic set increases, the level of retrieval performance across systems declines for that set. Conversely, as the visual dispersion of topics decreases, system performance rises. In common elements of conferences 2, 5, and 8, this relationship appears to hold despite increases in the number of participating systems in TREC. It is proposed that visual dispersion measures should be used to describe topic set difficulty in addition to measures such as “hardness”. 1 This study was supported by Intel Corporation. 2 A color version of this paper is available at . 3 Correspondence should be sent to the author at mrorvig@unt.edu. Introduction In the middle of a wonderful review article of the work of Project Intrex from 1965 to 1973, the authors interject this startling phrase: “Our analysis has shown that choice of words used in search strategies has a major influence on retrieval effectiveness” (Overhage and Reintjes, 1974, p. 174). This phrase startles because it is at once a reduction and an enigma. There is no doubt that word choice is important, but how can it be so important that retrieval performance depends upon it to the exclusion of so many other system and architecture considerations? The query is often the last item considered in IR testing. Usually its study is incorporated in the interaction effects between systems and users; a difficult and fluid arena. The suspicion that queries might establish a system performance limit did not arise in TREC literature until conference 5 (Voorhees and Harman, 1997). However, it has since been recognized as an area for important study, resulting in the establishment of a query track since conference 7 (Buckley, 1998). It is difficult to quantify the meaning of topic difficulty. Voorhees and Harman (1997) note that it is weakly (r = 0.33) correlated with the percent of unique relevant documents for that query. In the same volume, Sparck Jones remarks that “...low levels of performance...in TREC 4 and 5 must be taken as representing a more realistic retrieval situation than TREC 2 and 3...” (Sparck Jones, 1997, p. B-2). Sparck Jones comments further Retrieval Performance and Visual Dispersion of Query Sets in her review of TREC7 that “Since TREC-7 full topics are shorter than TREC-6, but TREC-7 performance levels are better, the TREC-7 topics are presumably not as hard...However, performance is not as tightly correlated with topic length, and specifically with version, as might be expected...” (Sparck Jones, 1999, p. B-6). Factors that cannot describe query difficulty are: (1) topic components (concepts, narratives, etc.), (2) topic length, (3) and topic construction (creating topics without regard to existing documents vs. the contrary practice). Document uniqueness is the only quantitative measure so far offered. Indeed, topic hardness appears to rest in that zone of phenomena that many can mutually observe, but cannot describe in terms that would eventually permit control. This paper proposes an additional quantitative measure for query difficulty. The measure is applicable to sets of topics only, but is based on the scaled similarity of documents by text terms. The proposed measure is replicable, and conforms to observed system performance behavior across three representative TREC conferences. Methodology TREC Topics were copied from the trec.nist.gov site and parsed into individual documents. A document similarity matrix was created using the cosine vector measure of similarity. The similarity matrix was scaled using maximum likelihood method customary for text data (Rorvig, 1999a) and plotted using a conventional graphics tool. Figure 1: Each dot in the illustration above represents a TREC topic. Arrayed from left to right, topic sets reveal increasing dispersion from topic set 3 onward. This effect does not change until topic sets 7 and 8 appear. -3 -2 -1 0 1 2 3 -6 -4 -2 0 2 4 TREC Topics 1-450 ad hoc RtgTr