Analysis of systems' performance in natural language processing competitions.
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| Title: | Analysis of systems' performance in natural language processing competitions. |
|---|---|
| Authors: | Nava-Muñoz, Sergio1,2 (AUTHOR) nava@cimat.mx, Graff, Mario2,3 (AUTHOR) mario.graff@infotec.mx, Escalante, Hugo Jair4 (AUTHOR) hugojair@inaoep.mx |
| Source: | Pattern Recognition Letters. Oct2024, Vol. 186, p346-353. 8p. |
| Subjects: | Natural language processing, Evaluation methodology, Natural languages, Confidence intervals, Contests |
| Abstract: | Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. This manuscript describes an evaluation methodology for statistically analyzing competition results and competition. The methodology is designed to be universally applicable; however, it is illustrated using eight natural language competitions as case studies involving classification and regression problems. The proposed methodology offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. Furthermore, we introduce metrics that allow organizers to assess the difficulty of competitions. Our analysis shows the potential usefulness of our methodology for effectively evaluating competition results. • A procedure using bootstrapping to analyze competition performance is presented. • Advanced tools & visuals to enhance score ranking for competitions' winner selection are described. • A study analyzing several NLP competitions using the methods and tools is described. • We highlight the need for robusts and effective tools when comparing the performance of models in competitions. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 181191320 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analysis of systems' performance in natural language processing competitions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nava-Muñoz%2C+Sergio%22">Nava-Muñoz, Sergio</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> nava@cimat.mx</i><br /><searchLink fieldCode="AR" term="%22Graff%2C+Mario%22">Graff, Mario</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> mario.graff@infotec.mx</i><br /><searchLink fieldCode="AR" term="%22Escalante%2C+Hugo+Jair%22">Escalante, Hugo Jair</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> hugojair@inaoep.mx</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition+Letters%22">Pattern Recognition Letters</searchLink>. Oct2024, Vol. 186, p346-353. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+languages%22">Natural languages</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Contests%22">Contests</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. This manuscript describes an evaluation methodology for statistically analyzing competition results and competition. The methodology is designed to be universally applicable; however, it is illustrated using eight natural language competitions as case studies involving classification and regression problems. The proposed methodology offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. Furthermore, we introduce metrics that allow organizers to assess the difficulty of competitions. Our analysis shows the potential usefulness of our methodology for effectively evaluating competition results. • A procedure using bootstrapping to analyze competition performance is presented. • Advanced tools & visuals to enhance score ranking for competitions' winner selection are described. • A study analyzing several NLP competitions using the methods and tools is described. • We highlight the need for robusts and effective tools when comparing the performance of models in competitions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181191320 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patrec.2024.03.010 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 346 Subjects: – SubjectFull: Natural language processing Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Natural languages Type: general – SubjectFull: Confidence intervals Type: general – SubjectFull: Contests Type: general Titles: – TitleFull: Analysis of systems' performance in natural language processing competitions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nava-Muñoz, Sergio – PersonEntity: Name: NameFull: Graff, Mario – PersonEntity: Name: NameFull: Escalante, Hugo Jair IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 01678655 Numbering: – Type: volume Value: 186 Titles: – TitleFull: Pattern Recognition Letters Type: main |
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