3.3 Test Collections

The test collections we employ in this thesis are described in the following sections. We use the Lemur Toolkit for indexing, retrieval, and all language modeling calculations.1 1See http://sourceforge.net/projects/lemur. For all test collections we use only the topic titles as queries. The test collections described first are used for our experiments in Chapters 4 and 7. For all of these collections, we remove a modest list of around 400 stopwords. Our retrieval model presented in Chapter 5 requires collections in which the documents have been manually annotated with an appropriate concept language. The test collections that we describe last (CLEF-DS and TREC-GEN) both satisfy this requirement.

Below we provide a more fine-grained description of each test collection. Tables 3.23.3, and 3.4 list descriptive statistics from each test collection.


( ×106) Size μ σ m μ σ m

TREC Rob 2004

0.5 2 GB 510 871 359 - - -


25 426 GB 956 2723 326 - - -

ClueWeb09 cat. A

500 13.4 TB 748 975 460 - - -

ClueWeb09 cat. B

50 1.5 TB 857 1186 507 - - -


0.17 232 MB 62 42 51 10.1 4.2 10


4.6 20 GB 174 114 171 11.4 5.1 11


0.16 12 GB 4160 2750 4525 15.1 6.1 15

Table 3.2: Statistics of the document collections used in this thesis. μ and m indicate the average and median number of terms in, or concepts assigned to a document respectively and σ the standard deviation. The second group of collections are domain-specific and contain manually assigned concepts as document annotations.

3.3.1 TREC Robust 2004

The first is TREC Robust 2004 (TREC-ROB-04), comprising a relatively small document collection and topics which were selected because of their low performance in the TREC ad hoc task [329]. It is the smallest of all collections used in this thesis and contains TREC disks 4 and 5, minus the Congressional Record [329]. The documents are small news articles from the Financial Times, Federal Register, LA Times, and Foreign Broadcast Information Service, covering 1989 through 1996. It is a collection that is routinely used when evaluating the performance of relevance feedback algorithms; 200 of its 250 topics were selected from earlier TREC ad hoc tracks based on their relatively poor performance and the ineffectiveness of relevance feedback techniques; 50 new topics were developed especially for the track.

3.3.2 TREC Terabyte 2004–2006

The second document collection is .GOV2, used in the TREC Terabyte, Million Query, and Relevance Feedback tracks [4855]; it contains a crawl of websites from the .gov domain. The TREC Terabyte track ran from 2004 through 2006 and used the first substantially sized TREC document collection [55]; its goal was to develop an evaluation methodology for terabyte-scale document collections. As topic set for this test collection (TREC-TB) we use the combined topics from all years.

3.3.3 TREC Relevance Feedback 2008

This test collection comprises test data provided by the TREC Relevance Feedback track, where the task is to retrieve additional relevant documents given a query and an initial set of relevance assessments [48]. Retrieval is done on the TREC Terabyte collection (the .GOV2 corpus) using 264 topics taken from earlier TREC Terabyte and TREC Million Query tracks [455].

For our explicit relevance feedback experiments (TREC-RF-08) we take the 33 TREC Terabyte topics which were selected from the full set of available topics for an additional round of assessments [48]. A large set of relevance assessments was provided for these topics (159 relevant documents on average, with a minimum of 50 and a maximum of 338). Participating systems were to return 2500 documents, from which the initially provided relevant documents were removed, a procedure similar to residual ranking (when performing residual ranking, all judged documents are removed—instead of only the relevant ones). The resulting rankings were then pooled and re-assessed. This yielded 55 new relevant documents on average per topic, with a minimum of 4 and a maximum of 177. We follow the same setup by keeping only the newly assessed, relevant documents for evaluation and discard all initially judged documents from the final rankings in our experiments.

In order to evaluate pseudo relevance feedback on this test collection (TREC-PRF-08), we use all 264 topics and the combined relevance assessments, i.e., the “original” pools and the new assessments.

3.3.4 TREC Web 2009

The fourth ad hoc test collection that we use has ClueWeb09 as its document collection (TREC-WEB-09). It was employed at the TREC 2009 and 2010 Web Track [72]. It is a large-scale web crawl and contains the largest number of documents. Two subsets are identified; Category B (that contains over 50,000,000 English web pages) and Category A (that contains over 500,000,000 English web pages). In 2009, participating runs were evaluated using shallow pools and the methodology introduced by the TREC Million Query track [46162] as introduced above. The 50 ad hoc topics are taken from a web search engine’s query logs.


With(out) rel. docs μ
Min. Max.


249 (1) 2 0.71 1 5


149 (0) 3 0.88 1 5


264 (0) 3 1.0 1 8


31 (2) 3 1.0 1 6


49 (1) 1 0.85 1 4


25 (0) 4 1.6 2 8


25 (0) 3 1.7 2 8


50 (0) 5 3.0 1 16


49 (1) 5 2.6 2 12


26 (2) 5 2.5 2 12

Table 3.3: Statistics of the topic sets used in this thesis.

Per topic



17412 70 3 448


26917 180 4 617


12639 47 4 457


1723 55 4 177

TREC-WEB-2009 (Cat. A)

5684 116 2 260

TREC-WEB-2009 (Cat. B)

4002 82 2 179


4530 181 18 497


2133 85 4 206


8268 165 1 697


4584 93 2 709


1449 55 2 234

Table 3.4: Statistics of the relevant documents per collection used in this thesis.

3.3.5 CLEF Domain-Specific 2007–2008

The CLEF domain-specific track evaluates retrieval on structured scientific documents, using bibliographic databases from the social sciences domain as document collections [244245]. The track emphasizes leveraging the structure of data in collections (defined by concept languages) to improve retrieval performance. The 2007 (CLEF-DS-07) and 2008 (CLEF-DS-08) tracks use the combined German Indexing and Retrieval Testdatabase (GIRT) and Cambridge Scientific Abstracts (CSA) databases as their document collection. The GIRT database contains extracts from two databases maintained by the German Social Science Information Centre from the years 1990–2000. The English GIRT collection is a pseudo-parallel corpus to the German GIRT collection, providing translated versions of the German documents (17% of these documents contain an abstract). For the 2007 domain-specific track, an extract from CSA’s Sociological abstracts was added, covering the years 1994, 1995, and 1996. Besides the title and abstract, each CSA record also contains subject-describing keywords from the CSA Thesaurus of Sociological Indexing Terms and classification codes from the Sociological Abstracts classification. In this sub-collection, 94% of the records contains an abstract. We only use the English mono-lingual topics and relevance assessments, which amounts to a total of 50 test topics. The documents in the collection contain three separate fields with concepts, we use CLASSIFICATION-TEXT-EN.

3.3.6 TREC Genomics 2004–2006

The document collection for the TREC 2004 and 2005 Genomics ad hoc search task (TREC-GEN-04 and TREC-GEN-05) consists of a subset of the MEDLINE database [129130]. MEDLINE is the bibliographic database maintained by the U.S. National Library of Medicine (NLM). At the time of writing, it contains over 18.5 million biomedical citations from around 5,500 journals and several hundred thousand records are added each year. Despite the growing availability of full-text articles on the Web, MEDLINE remains a central access point for biomedical literature. Each MEDLINE record contains free text fields (such as title and abstract), a number of fields containing other metadata (such as publication date and journal), and, most important for our model in Chapter 5, terms from the MeSH thesaurus. We only use the main descriptors, without qualifiers. MeSH terms are manually assigned to citations by trained annotators from the NLM. The over 20,000 biomedical concepts in MeSH are organized hierarchically, see Figure 1.2 for an example. Relationships between concepts are primarily of the “broader/narrower than” type. The “narrower than” relationship is close to expressing hypernymy (is a), but can also include meronymy (part of) relations. One concept is narrower than another if the documents it is assigned to are contained in the set of documents assigned to the broader term. Each MEDLINE record is annotated with 10–12 MeSH terms on average.

It should be noted that the MeSH thesaurus is not the most appropriate for Genomics information retrieval, since it covers general biomedical concepts rather than the specific genomics terminology used in the TREC topics [305]. Despite this limited coverage, the thesaurus can still be used to improve retrieval effectiveness, as we will show later.

The document collection for TREC Genomics 2004 and 2005 contains 10 years of citations covering 1993 to 2004, which amounts to a total of 4,591,008 documents. All documents have a title, 75.8% contain an abstract and 99% are annotated with MeSH terms. For the 2004 track, 50 test topics are available, with an average length of 7 terms, cf. Table 3.3. The 50 topics for 2005 (one of which has no relevant documents) follow pre-defined templates, so-called Generic Topic Types. An example of such a template is: “Find articles describing the role of [gene] in [disease]”, where the topics instantiate the bold-faced terms. The topics in our experiments are derived from the original topic by only selecting the instantiated terms and discarding the remainder of the template.

The TREC 2006 Genomics track introduced a full-text document collection, replacing the bibliographical abstracts from the previous years [131]. The documents in the collection are full-text versions of scientific journal papers. The files themselves are provided as HTML, including all the journal-specific formatting. Most of the documents (99%) have a valid Pubmed identifier through which the accompanying MEDLINE record can be retrieved. We use the MeSH terms assigned to the corresponding citation as the annotations of the full-text document.

The 2006 test topics are again based on topic templates and instantiated with specific genes, diseases or biological processes. Thus, we preprocess them in a similar fashion as the topics for the TREC Genomics 2005 track, by removing all the template-specific terms. This test collection has 28 topics, of which 2 do not have any relevant documents in the collection. The task put forward for this test collection is to first identify relevant documents and then extract the most relevant passage(s) from each document; relevance is measured at the document, passage, and aspect level. We do not perform any passage extraction and only use the judgments at the document level.1 12007 was the final year of the TREC Genomics track and used the same document collection as 2006. However, in this edition a new task was introduced and because of the different nature of that task, we do not perform experiments using the 2007 topics.