Dataset Open Access
The Webis Query Segmentation Corpus 2010 (Webis-QSeC-10) contains segmentations for 53,437 web queries obtained from Mechanical Turk crowdsourcing (4,850 used for training in our CIKM 2012 paper). For each query, at least 10 MTurk workers were asked to segment the query. The corpus represents the distribution of their decisions.
We provide the training and test sets as single folders in Zip archives containing several files. The files "...-queries.txt" contain the query strings and a unique ID for each query. The files "...-segmentations-crowdsourced.txt" contain the crowdsourced segmentations with their number of votes per query ID (see below for an example). The "data" folders contain all the data (n-gram frequencies, PMI values, POS tags, etc.) needed to replicate the evaluation results of our proposed segmentation algorithms. For convenience reasons, the folder "segmentations-of-algorithms" contain the segmentations that our proposed algorithms compute.
The original queries were extracted from the AOL query log, and range from 3 to 10 keywords in length. For each query at least 10 MTurk workers were asked to segment the query and their decisions are accumulated in the corpus. The examples below demonstrate two different cases.
Sample queries with internal ID (as in "Webis-QSeC-10-training-set-queries.txt"):
Sample segmentations (as in "webis-qsec-10-training-set-segmentations-crowdsourced.txt"):
Each query has a unique internal ID (e.g., 2315313155 in the first example) and the segmentations file contains at least 10 different decisions the MTurk workers made for that query. In the first example, 6 workers have all 4 keywords in one segment, 2 workers decided to break after the second word (denoted by a |) etc. Note that apostrophe in the second example (query ID 1858084875) is escaped by double quotes around the segmentation strings.
Matthias Hagen, Martin Potthast, Benno Stein, and Christof Bräutigam. The Power of Naïve Query Segmentation. In Fabio Crestani et al, editors, 33rd International ACM Conference on Research and Development in Information Retrieval (SIGIR 10), pages 797-798, July 2010. ACM. ISBN 978-1-4503-0153-4.