We provide the following data sets under a Creative Commons Attribution-ShareAlike 3.0 Unported License. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License.
Please cite this data set as
Laura Dietz, Ben Gamari. "TREC CAR 2.0: A Data Set for Complex Answer Retrieval". Version 2.0, 2018. http://trec-car.cs.unh.edu
Note that there are significant changes between this dataset and v1.5 regarding paragraph and entity ids.
All archives use XZ compression, datasize refers to uncompressed data.
paragraphCorpus.v2.0.tar.xz paragraph collection covering both train and test articles, entities of all types, and paragraphs from all content sections and lead paragraphs.
benchmarkY1-test-public.v2.0.tar.xz Official evaluation topics for TREC CAR 2017. Test set for Y1 benchmark, includes outlines only. For qrels for the test set see datasets below.
benchmarkY1-train.v2.0.tar.xz Train set for Y1 benchmark. Includes outlines, articles, qrels. (Selected in an with the same process as the test set.)
train.v2.0.tar.xz: Articles from 50% of Wikipedia that match the selection criterion. This half is dedicated for training any data-hungry machine learning methods. Data provided as articles, outlines, and qrels. Additionally, segmentation of the data into five folds for cross validation is provided in this archive.
test200.v2.0.tar.xz A manual selection of 200 pages from fold0, provided for training.
unprocessedTrain.v2.0.tar.xz Minimally processed version of the 50% of Wikipedia for training. Includes all page types, all sections, etc. Also includes images embedded on the page. Feel free to use this to derive knowledge bases or map to a knowledge base of your choice. This archive includes a list of “legal” wikipedia page titles for filtering of external resources.
unprocessedAllButBenchmark.v2.0.tar Like unprocessedTrain, but contains nearly everything of Wikipedia, only omitting pages in the test200 and benchmarkY1 benchmarks.
benchmarkY1Test-manual-qrels-v2.0.tar.xz (NEW!) Manual NIST assessments from 2017, translated to paragraph ids and entity ids of the V2.0 release.
benchmarkY1-test.tar.xz (NEW!) Automatic ground truth and full articles for the official evaluation topics for TREC CAR 2017.
For discontinued data releases see:
Support tools for reading this data can be found here: https://github.com/TREMA-UNH/trec-car-tools-java.
Don’t copy the code, use it as maven dependency:
<repositories> <repository> <id>jitpack.io</id> <url>https://jitpack.io</url> </repository> </repositories> <dependencies> ... <dependency> <groupId>com.github.TREMA-UNH</groupId> <artifactId>trec-car-tools-java</artifactId> <version>9</version> </dependency> ...
See examples on how to use the tools here: https://github.com/TREMA-UNH/trec-car-tools/.
You may be abel to lead v1.5 data files with this code, but we can’t guarantee correctness.
The following kinds of data derivatives are provided
*.cbor: full articles as derived from Wikipedia (after cleaning)
*.cbor-outlines.cbor: outlines of articles (=queries), preserving the hierarchy with sectionIDs.
*.cbor-paragraphs.cbor: paragraphs from these articles (=documents), with paragraph ids. These are intended for only training. (Evaluation will run based on the collection of paragraphs from the whole Wikipedia (see release-v1.1.cbor.paragraphs)
Qrels (trec_eval-compatible qrels files) which are automatically derived from Articles (to be complemented by human judgments).
*.cbor.hierarchical.qrels: every paragraph is relevant only for its leaf most specific section (example: PageName/Heading1/Heading1.1 - but not PageName/Heading1!)
*.cbor.toplevel.qrels: every paragraph is relevant only for its top-level section (the above example is relevant for PageName/Heading1, any second-level heading is ignored.)
*.cbor.articles.qrels: every paragraph is relevant for its article (the above example is relevant for PageName) - any section info is ignored.
*.cbor.hierarchical.entity.qrels: every entity is relevant only for its leaf most specific section (example: PageName/Heading1/Heading1.1 - but not PageName/Heading1!)
*.cbor.toplevel.entity.qrels: every entity is relevant only for its top-level section (the above example is relevant for PageName/Heading1, any second-level heading is ignored.)
*.cbor.articles.entity.qrels: every entity is relevant for its article (the above example is relevant for PageName) - any section info is ignored.
For evaluation, only a *cbor.outlines fill will be distributed and a decision will have been made which of the three qrels files will be used.
Please submit any issues with trec-car-tools as well as the provided data sets using the issue tracker on github.
TREC-CAR Dataset by Laura Dietz, Ben Gamari is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Based on a work at www.wikipedia.org.