Paper Info
Title
Nested Named Entity Recognition in Historical Archive Text
Abstract
This paper describes work on Named Entity Recognition (NER), in preparation for Relation Extraction (RE), on data from a historical archive organisation. As is often the case in the cultural heritage domain, the source text includes a high percentage of specialist terminology, and is of very variable quality in terms of grammaticality and completeness. The NER and RE tasks were carried out using a specially annotated corpus, and are themselves preliminary steps in a larger project whose aim is to transform discovered relations into a graph structure that can be queried using standard tools. Experimental results from the NER task are described, with emphasis on dealing with nested entities using a multi-word token method. The overall objective is to improve access by non-specialist users to a valuable cultural resource.
Year
DOI
Venue
2007
10.1109/ICSC.2007.63
ICSC
Keywords
Field
DocType
relation extraction,source text,information retrieval systems,relational databases,cultural heritage
Data mining,Relational database,Cultural heritage,Computer science,Natural language processing,Artificial intelligence,Relationship extraction,Entity linking,Information retrieval,Terminology,Grammaticality,Source text,Named-entity recognition
Conference
ISBN
Citations 
PageRank 
0-7695-2997-6
23
0.98
References 
Authors
12
1
Name
Order
Citations
PageRank
Kate Byrne1594.36