âIâd Rather Be Peggy than Bettyâ

Agirre, Katixa

doi:10.1057/9781137384669.0015

Cited by 2 publications

(3 citation statements)

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“…The task has been used as a community evaluation exercise, the *SEM 2013 shared task on Semantic Textual Similarity (Agirre et al, 2013b). The exercise attracted 14 system runs from six teams.…”

Section: Discussionmentioning

confidence: 99%

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Why are these similar? Investigating item similarity types in a large digital library

González-Agirre

Rigau

Agirre

et al. 2015

Asso for Info Science & Tech

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We introduce a new problem, identifying the type of relation that holds between a pair of similar items in a digital library. Being able to provide a reason why items are similar has applications to recommendation, personalisation and search.We investigate the problem within the context of Europeana, a large digital library containing items related to cultural heritage. A range of types of similarity in this collection were identified. A set of 1500 pairs of items from the collection were annotated using crowdsourcing. A high inter-tagger agreement (average 71.5 Pearson correlation) was obtained and demonstrates that the task is well defined. We also present several approaches to automatically identifying the type of similarity. The best system applies linear regression and achieves a mean Pearson correlation of 71.3, close to human performance.The problem formulation and data set described here were used in a public evaluation exercise, the *SEM shared task on Semantic Textual Similarity. The task attracted the participation of 6 teams, who submitted 14 system runs. All annotations, evaluation scripts and system runs are freely available 1 .

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Section: Discussionmentioning

confidence: 99%

“…The systems described in this article participated in the *SEM 2013 shared task (Agirre, Cer, Diab, Gonzalez-Agirre, & Guo, 2013b). Our baseline system was used as the overall task baseline against which all runs were compared.…”

Section: Performance In Shared Taskmentioning

confidence: 99%

Why are these similar? Investigating item similarity types in a large digital library

González-Agirre

Rigau

Agirre

et al. 2015

Asso for Info Science & Tech

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“…The fourth step compares each statement in the test paper T to potential equivalents in the set of statements from prior works C. For a human analyst, deciding whether two natural language statements are equivalent can require in-depth understanding of the concepts and relationships involved and may require careful consideration of nuance and context, leading to variance even in human-generated ratings of semantic similarity, as seen in (Agirre et al, 2016). Despite these challenges, automated semantic equivalence detection systems continue to advance, as we can see from the improvement in top scores between (Agirre, Diab, Cer, & Gonzalez-Agirre, 2012) and (Agirre et al, 2016).…”

Section: Strategy For Validation Of Fair Metricsmentioning

confidence: 99%

Definitions, formulas, and simulated examples for plagiarism detection with FAIR metrics

Craig

Ambati

Dutta

et al. 2019

Proceedings of the Association for Information Science and Tech

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In prior work, we proposed a family of metrics as a tool to quantify adherence to or deviation from good citation practices in scholarly research and publishing. We called this family of metrics FAIR as an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, and introduced definitions for these metrics with counts of instances of correct or incorrect attribution or nonattribution in primary research articles with citations for previously published references. In the present work, we extend our FAIR family of metrics by introducing a collection of ratio‐based metrics to accompany the count‐based metrics described previously. We illustrate the mathematical properties of the ratio‐based metrics with various simulated examples in order to assess their suitability as a means of identifying papers under peer review as more or less likely to be suspicious for plagiarism. These FAIR metrics would alert peer reviewers to prioritize low‐scoring manuscripts for closer scrutiny. Finally, we outline our planned strategy for future validation of the FAIR metrics with an approach using both expert human analysts and automated algorithms for computerized analysis.

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âIâd Rather Be Peggy than Bettyâ

Cited by 2 publications

References 0 publications

Why are these similar? Investigating item similarity types in a large digital library

Why are these similar? Investigating item similarity types in a large digital library

Definitions, formulas, and simulated examples for plagiarism detection with FAIR metrics

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âIâd Rather Be Peggy than Bettyâ

Cited by 2 publications

References 0 publications

Why are these similar? Investigating item similarity types in a large digital library

Why are these similar? Investigating item similarity types in a large digital library

Definitions, formulas, and simulated examples for plagiarism detection with FAIR metrics

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âIâd Rather Be Peggy than Bettyâ