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|Year=2015
 
|Year=2015
 
|Month=Mai
 
|Month=Mai
|Booktitle=Proceedings of the 24rd International World Wide Web Conference (WWW 2015) Poster Track
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|Booktitle=24th International World Wide Web Conference (WWW 2015), Companion Volume
|Publisher=to appear
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|Publisher=ACM
 
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}}
 
{{Publikation Details
 
{{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 paper zhang.pdf,
 
|Projekt=XLiMe
 
|Projekt=XLiMe
 
|Forschungsgruppe=Wissensmanagement
 
|Forschungsgruppe=Wissensmanagement
 
}}
 
}}

Aktuelle Version vom 19. April 2015, 13:25 Uhr


Towards Entity Correctness, Completeness and Emergence for Entity Recognition


Towards Entity Correctness, Completeness and Emergence for Entity Recognition



Published: 2015 Mai

Buchtitel: 24th International World Wide Web Conference (WWW 2015), Companion Volume
Verlag: ACM

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 paper zhang.pdf

Projekt

XLiMe



Forschungsgruppe

Wissensmanagement


Forschungsgebiet