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{{Publikation Details
|Abstract=Linking unstructured text to knowledge bases (KBs) by mapping words or phrases to the corresponding entities in KBs, is the problem of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that exiting approaches mainly suffer from. Experimental results show that the proposed approach significantly outperforms the state-of-the-art approaches in terms of precision, F-measure, micro-accuracy and macro-accuracy, while still preserving high recall.
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|Abstract=Linking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that existing approaches mainly suffer from. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in terms of precision, F-measure, micro-accuracy and macro-accuracy, while still preserving high recall.
|Download=WWW 2015 submission 1741.pdf,
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|Download=WWW 2015 submission 1741.pdf,Paper.pdf,  
 
|Projekt=XLiMe
 
|Projekt=XLiMe
 
|Forschungsgruppe=Wissensmanagement
 
|Forschungsgruppe=Wissensmanagement
 
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Version vom 15. März 2015, 05:14 Uhr


Towards Entity Correctness, Completeness and Emergence for Entity Recognition


Towards Entity Correctness, Completeness and Emergence for Entity Recognition



Published: 2015 Mai

Buchtitel: Proceedings of the Companion Publication of the 24th International World Wide Web Conference (WWW 2015) Poster Track
Verlag: to appear

Referierte Veröffentlichung

BibTeX

Kurzfassung
Linking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that existing approaches mainly suffer from. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in terms of precision, F-measure, micro-accuracy and macro-accuracy, while still preserving high recall.

Download: Media:WWW 2015 submission 1741.pdf,Media:Paper.pdf

Projekt

XLiMe



Forschungsgruppe

Wissensmanagement


Forschungsgebiet