Named Entity Recognition with Bidirectional LSTM-CNNs
J. Chiu, and E. Nichols. (2015)cite arxiv:1511.08308Comment: To appear in Transactions of the Association for Computational Linguistics.
Abstract
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.
Description
Named Entity Recognition with Bidirectional LSTM-CNNs
%0 Generic
%1 chiu2015named
%A Chiu, Jason P. C.
%A Nichols, Eric
%D 2015
%K final subword thema:sequence_labeling
%T Named Entity Recognition with Bidirectional LSTM-CNNs
%U http://arxiv.org/abs/1511.08308
%X 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.
@misc{chiu2015named,
abstract = {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.},
added-at = {2018-11-21T16:52:35.000+0100},
author = {Chiu, Jason P. C. and Nichols, Eric},
biburl = {https://www.bibsonomy.org/bibtex/25e09f2cdca08d268f1bef1c5ad61bd34/florianpircher},
description = {Named Entity Recognition with Bidirectional LSTM-CNNs},
interhash = {a0c2c449765cdab9c224a31edd3254df},
intrahash = {5e09f2cdca08d268f1bef1c5ad61bd34},
keywords = {final subword thema:sequence_labeling},
note = {cite arxiv:1511.08308Comment: To appear in Transactions of the Association for Computational Linguistics},
timestamp = {2018-11-27T09:31:42.000+0100},
title = {Named Entity Recognition with Bidirectional LSTM-CNNs},
url = {http://arxiv.org/abs/1511.08308},
year = 2015
}