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Unsupervised Dense Information Retrieval with Contrastive Learning - Cronologia
2026-05-13T15:44:58Z
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Alesaccoia il 13:34, 24 nov 2024
2024-11-24T13:34:48Z
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|citazioni=615</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|citazioni=615</div></td></tr>
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<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Vedi anche: [[Contriever]]</ins></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Questo studio esplora l'information retrieval denso non supervisionato mediante l'apprendimento contrastivo. L'obiettivo è migliorare la similarità semantica e l'efficacia della ricerca, in particolare in situazioni con pochi dati di addestramento o in contesti multilingue. I risultati mostrano che questo approccio supera i metodi tradizionali come BM25 in molti casi e offre un promettente punto di partenza per l'addestramento con dati limitati. Inoltre, dimostra buone capacità di trasferimento cross-linguale, aprendo nuove possibilità per la ricerca in lingue a basse risorse.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Questo studio esplora l'information retrieval denso non supervisionato mediante l'apprendimento contrastivo. L'obiettivo è migliorare la similarità semantica e l'efficacia della ricerca, in particolare in situazioni con pochi dati di addestramento o in contesti multilingue. I risultati mostrano che questo approccio supera i metodi tradizionali come BM25 in molti casi e offre un promettente punto di partenza per l'addestramento con dati limitati. Inoltre, dimostra buone capacità di trasferimento cross-linguale, aprendo nuove possibilità per la ricerca in lingue a basse risorse.</div></td></tr>
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Alesaccoia
https://wiki.mindmaker.it/index.php?title=Unsupervised_Dense_Information_Retrieval_with_Contrastive_Learning&diff=4542&oldid=prev
Alesaccoia il 10:34, 24 nov 2024
2024-11-24T10:34:52Z
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</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l9">Riga 9:</td>
<td colspan="2" class="diff-lineno">Riga 9:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Questo studio esplora l'information retrieval denso non supervisionato mediante l'apprendimento contrastivo. L'obiettivo è migliorare la similarità semantica e l'efficacia della ricerca, in particolare in situazioni con pochi dati di addestramento o in contesti multilingue. I risultati mostrano che questo approccio supera i metodi tradizionali come BM25 in molti casi e offre un promettente punto di partenza per l'addestramento con dati limitati. Inoltre, dimostra buone capacità di trasferimento cross-linguale, aprendo nuove possibilità per la ricerca in lingue a basse risorse.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Questo studio esplora l'information retrieval denso non supervisionato mediante l'apprendimento contrastivo. L'obiettivo è migliorare la similarità semantica e l'efficacia della ricerca, in particolare in situazioni con pochi dati di addestramento o in contesti multilingue. I risultati mostrano che questo approccio supera i metodi tradizionali come BM25 in molti casi e offre un promettente punto di partenza per l'addestramento con dati limitati. Inoltre, dimostra buone capacità di trasferimento cross-linguale, aprendo nuove possibilità per la ricerca in lingue a basse risorse.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Pubblicazione]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Category:Pubblicazione]]</div></td></tr>
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Alesaccoia
https://wiki.mindmaker.it/index.php?title=Unsupervised_Dense_Information_Retrieval_with_Contrastive_Learning&diff=4541&oldid=prev
Mindmakerbot il 10:33, 24 nov 2024
2024-11-24T10:33:55Z
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<td colspan="2" class="diff-lineno">Riga 1:</td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{template pubblicazione</ins></div></td></tr>
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<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">|URL=https://www.semanticscholar.org/paper/4f4a409f701f7552d45c46a5b0fea69dca6f8e84</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">|topic=Information Retrieval</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">|citazioni=615</ins></div></td></tr>
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<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Questo studio esplora l'information retrieval denso non supervisionato mediante l'apprendimento contrastivo. L'obiettivo è migliorare la similarità semantica e l'efficacia della ricerca, in particolare in situazioni con pochi dati di addestramento o in contesti multilingue. I risultati mostrano che questo approccio supera i metodi tradizionali come BM25 in molti casi e offre un promettente punto di partenza per l'addestramento con dati limitati. Inoltre, dimostra buone capacità di trasferimento cross-linguale, aprendo nuove possibilità per la ricerca in lingue a basse risorse.</ins></div></td></tr>
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<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[Category:Pubblicazione]]</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{#seo:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{#seo:</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|title=Unsupervised Dense Information Retrieval with Contrastive Learning</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|title=Unsupervised Dense Information Retrieval with Contrastive Learning</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|title_mode=append</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|title_mode=append</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|keywords=<del style="font-weight: bold; text-decoration: none;">apprendimento automatico, </del>information retrieval, <del style="font-weight: bold; text-decoration: none;">contrastive learning</del>, <del style="font-weight: bold; text-decoration: none;">deep learning</del>, <del style="font-weight: bold; text-decoration: none;">intelligenza artificiale</del>, <del style="font-weight: bold; text-decoration: none;">NLP</del>, <del style="font-weight: bold; text-decoration: none;">elaborazione del linguaggio naturale</del>, <del style="font-weight: bold; text-decoration: none;">rappresentazione densa</del>, <del style="font-weight: bold; text-decoration: none;">similarità semantica</del>, <del style="font-weight: bold; text-decoration: none;">vettori</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|keywords=information retrieval, <ins style="font-weight: bold; text-decoration: none;">apprendimento contrastivo</ins>, <ins style="font-weight: bold; text-decoration: none;">similarità semantica, ricerca non supervisionata</ins>, <ins style="font-weight: bold; text-decoration: none;">pochi dati</ins>, <ins style="font-weight: bold; text-decoration: none;">multilingue</ins>, <ins style="font-weight: bold; text-decoration: none;">BM25</ins>, <ins style="font-weight: bold; text-decoration: none;">trasferimento cross-linguale</ins>, <ins style="font-weight: bold; text-decoration: none;">lingue a basse risorse</ins>, <ins style="font-weight: bold; text-decoration: none;">addestramento con dati limitati</ins></div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|description=<del style="font-weight: bold; text-decoration: none;">Questa pubblicazione esplora l</del>'information retrieval denso non supervisionato con <del style="font-weight: bold; text-decoration: none;">l'</del>apprendimento contrastivo. <del style="font-weight: bold; text-decoration: none;">Scopri </del>come <del style="font-weight: bold; text-decoration: none;">migliorare </del>la similarità semantica <del style="font-weight: bold; text-decoration: none;">e l'efficacia della ricerca</del>.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|description=<ins style="font-weight: bold; text-decoration: none;">Studio sull</ins>'information retrieval denso non supervisionato con apprendimento contrastivo. <ins style="font-weight: bold; text-decoration: none;"> Supera metodi tradizionali </ins>come <ins style="font-weight: bold; text-decoration: none;">BM25, migliorando </ins>la similarità semantica<ins style="font-weight: bold; text-decoration: none;">, specie in contesti multilingue o con pochi dati</ins>.</div></td></tr>
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Mindmakerbot
https://wiki.mindmaker.it/index.php?title=Unsupervised_Dense_Information_Retrieval_with_Contrastive_Learning&diff=4540&oldid=prev
Alesaccoia il 23:48, 23 nov 2024
2024-11-23T23:48:28Z
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Mindmakerbot il 23:47, 23 nov 2024
2024-11-23T23:47:37Z
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