Automatic ranking of retrieval models using retrievability measure
Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing diff...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bashir, Shariq [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London 2014 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Knowledge and information systems - Springer London, 2000, 41(2014), 1 vom: 01. Juni, Seite 189-221 |
|---|---|
| Übergeordnetes Werk: |
volume:41 ; year:2014 ; number:1 ; day:01 ; month:06 ; pages:189-221 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10115-014-0759-6 |
|---|
| Katalog-ID: |
OLC206337838X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC206337838X | ||
| 003 | DE-627 | ||
| 005 | 20230502172724.0 | ||
| 007 | tu | ||
| 008 | 200820s2014 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10115-014-0759-6 |2 doi | |
| 035 | |a (DE-627)OLC206337838X | ||
| 035 | |a (DE-He213)s10115-014-0759-6-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 082 | 0 | 4 | |a 004 |q VZ |
| 084 | |a 06.74$jInformationssysteme |2 bkl | ||
| 084 | |a 54.64$jDatenbanken |2 bkl | ||
| 100 | 1 | |a Bashir, Shariq |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic ranking of retrieval models using retrievability measure |
| 264 | 1 | |c 2014 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag London 2014 | ||
| 520 | |a Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). | ||
| 650 | 4 | |a Retrieval models evaluation | |
| 650 | 4 | |a Retrieval bias analysis | |
| 650 | 4 | |a Automatic ranking of retrieval models | |
| 650 | 4 | |a Genetic programming | |
| 700 | 1 | |a Rauber, Andreas |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Knowledge and information systems |d Springer London, 2000 |g 41(2014), 1 vom: 01. Juni, Seite 189-221 |w (DE-627)323971725 |w (DE-600)2036569-X |w (DE-576)9323971723 |x 0219-1377 |7 nnns |
| 773 | 1 | 8 | |g volume:41 |g year:2014 |g number:1 |g day:01 |g month:06 |g pages:189-221 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10115-014-0759-6 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_4277 | ||
| 936 | b | k | |a 06.74$jInformationssysteme |q VZ |0 106415212 |0 (DE-625)106415212 |
| 936 | b | k | |a 54.64$jDatenbanken |q VZ |0 106410865 |0 (DE-625)106410865 |
| 951 | |a AR | ||
| 952 | |d 41 |j 2014 |e 1 |b 01 |c 06 |h 189-221 | ||
| author_variant |
s b sb a r ar |
|---|---|
| matchkey_str |
article:02191377:2014----::uoairnigfereamdluiger |
| hierarchy_sort_str |
2014 |
| bklnumber |
06.74$jInformationssysteme 54.64$jDatenbanken |
| publishDate |
2014 |
| allfields |
10.1007/s10115-014-0759-6 doi (DE-627)OLC206337838X (DE-He213)s10115-014-0759-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Bashir, Shariq verfasserin aut Automatic ranking of retrieval models using retrievability measure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming Rauber, Andreas aut Enthalten in Knowledge and information systems Springer London, 2000 41(2014), 1 vom: 01. Juni, Seite 189-221 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:41 year:2014 number:1 day:01 month:06 pages:189-221 https://doi.org/10.1007/s10115-014-0759-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 GBV_ILN_4277 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 41 2014 1 01 06 189-221 |
| spelling |
10.1007/s10115-014-0759-6 doi (DE-627)OLC206337838X (DE-He213)s10115-014-0759-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Bashir, Shariq verfasserin aut Automatic ranking of retrieval models using retrievability measure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming Rauber, Andreas aut Enthalten in Knowledge and information systems Springer London, 2000 41(2014), 1 vom: 01. Juni, Seite 189-221 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:41 year:2014 number:1 day:01 month:06 pages:189-221 https://doi.org/10.1007/s10115-014-0759-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 GBV_ILN_4277 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 41 2014 1 01 06 189-221 |
| allfields_unstemmed |
10.1007/s10115-014-0759-6 doi (DE-627)OLC206337838X (DE-He213)s10115-014-0759-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Bashir, Shariq verfasserin aut Automatic ranking of retrieval models using retrievability measure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming Rauber, Andreas aut Enthalten in Knowledge and information systems Springer London, 2000 41(2014), 1 vom: 01. Juni, Seite 189-221 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:41 year:2014 number:1 day:01 month:06 pages:189-221 https://doi.org/10.1007/s10115-014-0759-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 GBV_ILN_4277 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 41 2014 1 01 06 189-221 |
| allfieldsGer |
10.1007/s10115-014-0759-6 doi (DE-627)OLC206337838X (DE-He213)s10115-014-0759-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Bashir, Shariq verfasserin aut Automatic ranking of retrieval models using retrievability measure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming Rauber, Andreas aut Enthalten in Knowledge and information systems Springer London, 2000 41(2014), 1 vom: 01. Juni, Seite 189-221 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:41 year:2014 number:1 day:01 month:06 pages:189-221 https://doi.org/10.1007/s10115-014-0759-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 GBV_ILN_4277 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 41 2014 1 01 06 189-221 |
| allfieldsSound |
10.1007/s10115-014-0759-6 doi (DE-627)OLC206337838X (DE-He213)s10115-014-0759-6-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Bashir, Shariq verfasserin aut Automatic ranking of retrieval models using retrievability measure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming Rauber, Andreas aut Enthalten in Knowledge and information systems Springer London, 2000 41(2014), 1 vom: 01. Juni, Seite 189-221 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:41 year:2014 number:1 day:01 month:06 pages:189-221 https://doi.org/10.1007/s10115-014-0759-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 GBV_ILN_4277 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 41 2014 1 01 06 189-221 |
| language |
English |
| source |
Enthalten in Knowledge and information systems 41(2014), 1 vom: 01. Juni, Seite 189-221 volume:41 year:2014 number:1 day:01 month:06 pages:189-221 |
| sourceStr |
Enthalten in Knowledge and information systems 41(2014), 1 vom: 01. Juni, Seite 189-221 volume:41 year:2014 number:1 day:01 month:06 pages:189-221 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Knowledge and information systems |
| authorswithroles_txt_mv |
Bashir, Shariq @@aut@@ Rauber, Andreas @@aut@@ |
| publishDateDaySort_date |
2014-06-01T00:00:00Z |
| hierarchy_top_id |
323971725 |
| dewey-sort |
14 |
| id |
OLC206337838X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC206337838X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502172724.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2014 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10115-014-0759-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC206337838X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10115-014-0759-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.64$jDatenbanken</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bashir, Shariq</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic ranking of retrieval models using retrievability measure</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Retrieval models evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Retrieval bias analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automatic ranking of retrieval models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic programming</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rauber, Andreas</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Knowledge and information systems</subfield><subfield code="d">Springer London, 2000</subfield><subfield code="g">41(2014), 1 vom: 01. Juni, Seite 189-221</subfield><subfield code="w">(DE-627)323971725</subfield><subfield code="w">(DE-600)2036569-X</subfield><subfield code="w">(DE-576)9323971723</subfield><subfield code="x">0219-1377</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:41</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:1</subfield><subfield code="g">day:01</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:189-221</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10115-014-0759-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="q">VZ</subfield><subfield code="0">106415212</subfield><subfield code="0">(DE-625)106415212</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.64$jDatenbanken</subfield><subfield code="q">VZ</subfield><subfield code="0">106410865</subfield><subfield code="0">(DE-625)106410865</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">41</subfield><subfield code="j">2014</subfield><subfield code="e">1</subfield><subfield code="b">01</subfield><subfield code="c">06</subfield><subfield code="h">189-221</subfield></datafield></record></collection>
|
| author |
Bashir, Shariq |
| spellingShingle |
Bashir, Shariq ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Retrieval models evaluation misc Retrieval bias analysis misc Automatic ranking of retrieval models misc Genetic programming Automatic ranking of retrieval models using retrievability measure |
| authorStr |
Bashir, Shariq |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)323971725 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0219-1377 |
| topic_title |
004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Automatic ranking of retrieval models using retrievability measure Retrieval models evaluation Retrieval bias analysis Automatic ranking of retrieval models Genetic programming |
| topic |
ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Retrieval models evaluation misc Retrieval bias analysis misc Automatic ranking of retrieval models misc Genetic programming |
| topic_unstemmed |
ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Retrieval models evaluation misc Retrieval bias analysis misc Automatic ranking of retrieval models misc Genetic programming |
| topic_browse |
ddc 004 bkl 06.74$jInformationssysteme bkl 54.64$jDatenbanken misc Retrieval models evaluation misc Retrieval bias analysis misc Automatic ranking of retrieval models misc Genetic programming |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Knowledge and information systems |
| hierarchy_parent_id |
323971725 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Knowledge and information systems |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 |
| title |
Automatic ranking of retrieval models using retrievability measure |
| ctrlnum |
(DE-627)OLC206337838X (DE-He213)s10115-014-0759-6-p |
| title_full |
Automatic ranking of retrieval models using retrievability measure |
| author_sort |
Bashir, Shariq |
| journal |
Knowledge and information systems |
| journalStr |
Knowledge and information systems |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2014 |
| contenttype_str_mv |
txt |
| container_start_page |
189 |
| author_browse |
Bashir, Shariq Rauber, Andreas |
| container_volume |
41 |
| class |
004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl |
| format_se |
Aufsätze |
| author-letter |
Bashir, Shariq |
| doi_str_mv |
10.1007/s10115-014-0759-6 |
| normlink |
106415212 106410865 |
| normlink_prefix_str_mv |
106415212 (DE-625)106415212 106410865 (DE-625)106410865 |
| dewey-full |
004 |
| title_sort |
automatic ranking of retrieval models using retrievability measure |
| title_auth |
Automatic ranking of retrieval models using retrievability measure |
| abstract |
Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). © Springer-Verlag London 2014 |
| abstractGer |
Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). © Springer-Verlag London 2014 |
| abstract_unstemmed |
Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments). © Springer-Verlag London 2014 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 GBV_ILN_4277 |
| container_issue |
1 |
| title_short |
Automatic ranking of retrieval models using retrievability measure |
| url |
https://doi.org/10.1007/s10115-014-0759-6 |
| remote_bool |
false |
| author2 |
Rauber, Andreas |
| author2Str |
Rauber, Andreas |
| ppnlink |
323971725 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10115-014-0759-6 |
| up_date |
2024-07-03T18:49:02.186Z |
| _version_ |
1803584842960470016 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC206337838X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502172724.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2014 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10115-014-0759-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC206337838X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10115-014-0759-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.64$jDatenbanken</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bashir, Shariq</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic ranking of retrieval models using retrievability measure</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models’ effectiveness on a given corpus without requiring any ground truth available (relevance judgments).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Retrieval models evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Retrieval bias analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automatic ranking of retrieval models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic programming</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rauber, Andreas</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Knowledge and information systems</subfield><subfield code="d">Springer London, 2000</subfield><subfield code="g">41(2014), 1 vom: 01. Juni, Seite 189-221</subfield><subfield code="w">(DE-627)323971725</subfield><subfield code="w">(DE-600)2036569-X</subfield><subfield code="w">(DE-576)9323971723</subfield><subfield code="x">0219-1377</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:41</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:1</subfield><subfield code="g">day:01</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:189-221</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10115-014-0759-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">06.74$jInformationssysteme</subfield><subfield code="q">VZ</subfield><subfield code="0">106415212</subfield><subfield code="0">(DE-625)106415212</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.64$jDatenbanken</subfield><subfield code="q">VZ</subfield><subfield code="0">106410865</subfield><subfield code="0">(DE-625)106410865</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">41</subfield><subfield code="j">2014</subfield><subfield code="e">1</subfield><subfield code="b">01</subfield><subfield code="c">06</subfield><subfield code="h">189-221</subfield></datafield></record></collection>
|
| score |
7.4007807 |