@inProceedings{
 title = {Nested Named Entity Recognition in Historical Archive Text},
 type = {inProceedings},
 year = {2007},
 identifiers = {[object Object]},
 pages = {589-596},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4338398},
 publisher = {Ieee},
 id = {c1d57cb8-6982-33d7-8b2a-c7e9cccc03dc},
 created = {2012-01-21T12:35:31.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Byrne2007},
 private_publication = {false},
 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.},
 bibtype = {inProceedings},
 author = {Byrne, Kate},
 booktitle = {C}
}

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