Cross-document coreference resolution using latent features

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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.. ed. / Anna Lisa Gentile; Ziqi Zhang; Claudia d'Amato; Heiko Paulheim. Vol. 1267 Sun Site Central Europe (RWTH Aachen University), 2014. p. 33-44 (CEUR Workshop Proceedings; Vol. 1267).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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 (eds), 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.. vol. 1267, CEUR Workshop Proceedings, vol. 1267, Sun Site Central Europe (RWTH Aachen University), pp. 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, Italy, 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 (Eds.), 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. (Vol. 1267, pp. 33-44). (CEUR Workshop Proceedings; Vol. 1267). Sun Site Central Europe (RWTH Aachen University). https://nbn-resolving.org/urn:nbn:de:0074-1267-1

Vancouver

Ngonga Ngomo AC, Röder M, Usbeck R. Cross-document coreference resolution using latent features. In Gentile AL, Zhang Z, d'Amato C, Paulheim H, editors, 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.. Vol. 1267. Sun Site Central Europe (RWTH Aachen University). 2014. p. 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 -

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