Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model

Hui, K., Yates, A., Berberich, K., & de Melo, G. (2017). RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model. Retrieved from http://arxiv.org/abs/1706.10192.

Item is

Basisdaten

einblenden: ausblenden:
Genre: Forschungspapier
Latex : {RE-PACRR}: {A} Context and Density-Aware Neural Information Retrieval Model

Dateien

einblenden: Dateien
ausblenden: Dateien
:
arXiv:1706.10192.pdf (Preprint), 703KB
Name:
arXiv:1706.10192.pdf
Beschreibung:
File downloaded from arXiv at 2017-10-13 10:26 Appear in Neu-IR workshop 2017
OA-Status:
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Hui, Kai1, Autor           
Yates, Andrew1, Autor           
Berberich, Klaus1, Autor           
de Melo, Gerard2, Autor
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
 Zusammenfassung: Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2017-06-302017-07-242017
 Publikationsstatus: Online veröffentlicht
 Seiten: 8 p.
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: arXiv: 1706.10192
URI: http://arxiv.org/abs/1706.10192
BibTex Citekey: HuiarXiv2017b
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle

einblenden: