One Class per Named Entity: Exploiting Unlabeled Text for Named Entity Recognition
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1763-1767.
Abstract
In this paper, we present a simple yet novel method of exploiting unlabeled text to further improve the accuracy of a high-performance state-of-the-art named entity recognition (NER) system. The method utilizes the empirical property that many named entities occur in one name class only. Using only unlabeled text as the additional resource, our improved NER system achieves an F1 score of 87.13%, an improvement of 1.17% in F1 score and a 8.3% error reduction on the CoNLL 2003 English NER official test set. This accuracy places our NER system among the top 3 systems in the CoNLL 2003 English shared task.
@inproceedings{ijcai2007p285,
title = {One Class per Named Entity: Exploiting Unlabeled Text for Named Entity Recognition},
author = {Wong, Yingchuan and Ng, Hwee Tou},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1763--1767},
year = {2007},
url = {https://ijcai.org/proceedings/2007/285},
}
TY - CPAPER TI - One Class per Named Entity: Exploiting Unlabeled Text for Named Entity Recognition AU - Wong, Yingchuan AU - Ng, Hwee Tou T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1763 EP - 1767 UR - https://ijcai.org/proceedings/2007/285 ER -Copied
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