Standard
Cross-document coreference resolution using latent features. / Ngonga Ngomo, Axel Cyrille; Röder, Michael
; Usbeck, Ricardo.
Linked Data for Information Extraction 2014. : Proceedings of the Second International Workshop on Linked Data for Information Extraction (LD4IE 2014), Riva del Garda, Italy, October 20, 2014.. Hrsg. / Anna Lisa Gentile; Ziqi Zhang; Claudia d'Amato; Heiko Paulheim. Band 1267 Sun Site Central Europe (RWTH Aachen University), 2014. S. 33-44 (CEUR Workshop Proceedings; Band 1267).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
Ngonga Ngomo, AC, Röder, M
& Usbeck, R 2014,
Cross-document coreference resolution using latent features. in AL Gentile, Z Zhang, C d'Amato & H Paulheim (Hrsg.),
Linked Data for Information Extraction 2014. : Proceedings of the Second International Workshop on Linked Data for Information Extraction (LD4IE 2014), Riva del Garda, Italy, October 20, 2014.. Bd. 1267, CEUR Workshop Proceedings, Bd. 1267, Sun Site Central Europe (RWTH Aachen University), S. 33-44, 2nd International Workshop on Linked Data for Information Extraction, LD4IE 2014, Co-located with the 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italien,
20.10.14. <
https://nbn-resolving.org/urn:nbn:de:0074-1267-1>
APA
Ngonga Ngomo, A. C., Röder, M.
, & Usbeck, R. (2014).
Cross-document coreference resolution using latent features. In A. L. Gentile, Z. Zhang, C. d'Amato, & H. Paulheim (Hrsg.),
Linked Data for Information Extraction 2014. : Proceedings of the Second International Workshop on Linked Data for Information Extraction (LD4IE 2014), Riva del Garda, Italy, October 20, 2014. (Band 1267, S. 33-44). (CEUR Workshop Proceedings; Band 1267). Sun Site Central Europe (RWTH Aachen University).
https://nbn-resolving.org/urn:nbn:de:0074-1267-1Vancouver
Ngonga Ngomo AC, Röder M
, Usbeck R.
Cross-document coreference resolution using latent features. in Gentile AL, Zhang Z, d'Amato C, Paulheim H, Hrsg., Linked Data for Information Extraction 2014. : Proceedings of the Second International Workshop on Linked Data for Information Extraction (LD4IE 2014), Riva del Garda, Italy, October 20, 2014.. Band 1267. Sun Site Central Europe (RWTH Aachen University). 2014. S. 33-44. (CEUR Workshop Proceedings).
Bibtex
@inbook{bcc2da8bb1774038b06d68895e786cea,
title = "Cross-document coreference resolution using latent features",
abstract = "Over the last years, entity detection approaches which combine named entity recognition and entity linking have been used to detect mentions of RDF resources from a given reference knowledge base in unstructured data. In this paper, we address the problem of assigning a single URI to named entities which stand for the same real-object across documents but are not yet available in the reference knowledge base. This task is known as cross-document co-reference resolution and has been addressed by manifold approaches in the past. We present a preliminary study of a novel take on the task based on the use of latent features derived from matrix factorizations combined with parameter-free graph clustering. We study the influence of different parameters (window size, rank, hardening) on our approach by comparing the F-measures we achieve on the N3 benchmark. Our results suggest that using latent features leads to higher F-measures with an increase of up to 20.5% on datasets of the N3 collection.",
keywords = "Informatics",
author = "{Ngonga Ngomo}, {Axel Cyrille} and Michael R{\"o}der and Ricardo Usbeck",
note = "European Science Foundation; 2nd International Workshop on Linked Data for Information Extraction, LD4IE 2014, Co-located with the 13th International Semantic Web Conference, ISWC 2014 ; Conference date: 20-10-2014",
year = "2014",
month = oct,
day = "15",
language = "English",
volume = "1267",
series = "CEUR Workshop Proceedings",
publisher = "Sun Site Central Europe (RWTH Aachen University)",
pages = "33--44",
editor = "Gentile, {Anna Lisa} and Ziqi Zhang and Claudia d'Amato and Heiko Paulheim",
booktitle = "Linked Data for Information Extraction 2014.",
address = "Germany",
url = "http://iswc2014.semanticweb.org/index.html",
}
RIS
TY - CHAP
T1 - Cross-document coreference resolution using latent features
AU - Ngonga Ngomo, Axel Cyrille
AU - Röder, Michael
AU - Usbeck, Ricardo
N1 - European Science Foundation
PY - 2014/10/15
Y1 - 2014/10/15
N2 - Over the last years, entity detection approaches which combine named entity recognition and entity linking have been used to detect mentions of RDF resources from a given reference knowledge base in unstructured data. In this paper, we address the problem of assigning a single URI to named entities which stand for the same real-object across documents but are not yet available in the reference knowledge base. This task is known as cross-document co-reference resolution and has been addressed by manifold approaches in the past. We present a preliminary study of a novel take on the task based on the use of latent features derived from matrix factorizations combined with parameter-free graph clustering. We study the influence of different parameters (window size, rank, hardening) on our approach by comparing the F-measures we achieve on the N3 benchmark. Our results suggest that using latent features leads to higher F-measures with an increase of up to 20.5% on datasets of the N3 collection.
AB - Over the last years, entity detection approaches which combine named entity recognition and entity linking have been used to detect mentions of RDF resources from a given reference knowledge base in unstructured data. In this paper, we address the problem of assigning a single URI to named entities which stand for the same real-object across documents but are not yet available in the reference knowledge base. This task is known as cross-document co-reference resolution and has been addressed by manifold approaches in the past. We present a preliminary study of a novel take on the task based on the use of latent features derived from matrix factorizations combined with parameter-free graph clustering. We study the influence of different parameters (window size, rank, hardening) on our approach by comparing the F-measures we achieve on the N3 benchmark. Our results suggest that using latent features leads to higher F-measures with an increase of up to 20.5% on datasets of the N3 collection.
KW - Informatics
UR - http://www.scopus.com/inward/record.url?scp=84939863962&partnerID=8YFLogxK
M3 - Article in conference proceedings
AN - SCOPUS:84939863962
VL - 1267
T3 - CEUR Workshop Proceedings
SP - 33
EP - 44
BT - Linked Data for Information Extraction 2014.
A2 - Gentile, Anna Lisa
A2 - Zhang, Ziqi
A2 - d'Amato, Claudia
A2 - Paulheim, Heiko
PB - Sun Site Central Europe (RWTH Aachen University)
T2 - 2nd International Workshop on Linked Data for Information Extraction, LD4IE 2014, Co-located with the 13th International Semantic Web Conference, ISWC 2014
Y2 - 20 October 2014
ER -
