Using Corpus-derived Name Lists for Named Entity Recognition (2000)
| Venue: | In Proc. of Applied NLP and the N. American Chapter of the ACL |
BibTeX
@INPROCEEDINGS{And00usingcorpus-derived,
author = {Mark Stevenson And and Mark Stevenson and Robert Gaizauskas},
title = {Using Corpus-derived Name Lists for Named Entity Recognition},
booktitle = {In Proc. of Applied NLP and the N. American Chapter of the ACL},
year = {2000},
pages = {290--295}
}
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Abstract
This paper describes experiments to establish the performance of a named entity recognition system which builds categorized lists of names from manually annotated training data. Names in text are then identified using only these lists. This approach does not perform as well as state-of-the-art named entity recognition systems. However, we then show that by using simple filtering techniques for improving the automatically acquired lists, substantial performance benefits can be achieved, with resulting Fmeasure scores of 87% on a standard test set. These results provide a baseline against which the contribution of more sophisticated supervised learning techniques for NE recognition should be measured. 1 Introduction Named entity (NE) recognition is the process of identifying and categorising names in text. Systems which have attempted the NE task have, in general, made use of lists of common names to provide clues. Name lists provide an extremely efficient way of recognising names, as ...
Keyphrases
named entity recognition corpus-derived name list entity recognition system ne recognition resulting fmeasure score training data acquired list substantial performance benefit categorized list standard test set sophisticated supervised learning technique ne task common name introduction named entity efficient way
