Named Entity Recognition with Bidirectional LSTM-CNNs
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level fe...
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Published in | Transactions of the Association for Computational Linguistics Vol. 4; pp. 357 - 370 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.12.2016
MIT Press Journals, The |
Subjects | |
Online Access | Get full text |
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Summary: | Named entity recognition is a challenging task that has traditionally required
large amounts of knowledge in the form of feature engineering and lexicons to
achieve high performance. In this paper, we present a novel neural network
architecture that automatically detects word- and character-level features using
a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most
feature engineering. We also propose a novel method of encoding partial lexicon
matches in neural networks and compare it to existing approaches. Extensive
evaluation shows that, given only tokenized text and publicly available word
embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses
the previously reported state of the art performance on the OntoNotes 5.0
dataset by 2.13 F1 points. By using two lexicons constructed from
publicly-available sources, we establish new state of the art performance with
an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems
that employ heavy feature engineering, proprietary lexicons, and rich entity
linking information. |
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Bibliography: | Volume, 2016 |
ISSN: | 2307-387X 2307-387X |
DOI: | 10.1162/tacl_a_00104 |