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ZENODO
Dataset . 2010
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2010
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2010
License: CC BY
Data sources: ZENODO
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Webis Query Segmentation Corpus 2010 (Webis-QSeC-10)

Authors: Hagen, Matthias; Potthast, Martin; Stein, Benno; Bräutigam, Christof; Beyer, Anna;

Webis Query Segmentation Corpus 2010 (Webis-QSeC-10)

Abstract

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"): 2315313155 harvard community credit union 1858084875 women's cycling tops Sample segmentations (as in "webis-qsec-10-training-set-segmentations-crowdsourced.txt"): 2315313155 [(6, 'harvard community credit union'), (2, 'harvard community|credit union'), (1, 'harvard|community|credit union'), (1, 'harvard|community credit union')] 1858084875 [(5, "women's|cycling tops"), (2, "women's|cycling|tops"), (2, "women's cycling|tops"), (1, "women's cycling tops")] 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.

{"references": ["Matthias Hagen, Martin Potthast, Benno Stein, and Christof Br\u00e4utigam. The Power of Na\u00efve 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."]}

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Keywords

AOL, query, web queries, segmentation

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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