Cargando…

Long short-term memory RNN for biomedical named entity recognition

BACKGROUND: Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for...

Descripción completa

Detalles Bibliográficos
Autores principales: Lyu, Chen, Chen, Bo, Ren, Yafeng, Ji, Donghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663060/
https://www.ncbi.nlm.nih.gov/pubmed/29084508
http://dx.doi.org/10.1186/s12859-017-1868-5
_version_ 1783274755597533184
author Lyu, Chen
Chen, Bo
Ren, Yafeng
Ji, Donghong
author_facet Lyu, Chen
Chen, Bo
Ren, Yafeng
Ji, Donghong
author_sort Lyu, Chen
collection PubMed
description BACKGROUND: Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features. RESULTS: We present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus. CONCLUSIONS: Our neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER.
format Online
Article
Text
id pubmed-5663060
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-56630602017-11-01 Long short-term memory RNN for biomedical named entity recognition Lyu, Chen Chen, Bo Ren, Yafeng Ji, Donghong BMC Bioinformatics Research Article BACKGROUND: Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features. RESULTS: We present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus. CONCLUSIONS: Our neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER. BioMed Central 2017-10-30 /pmc/articles/PMC5663060/ /pubmed/29084508 http://dx.doi.org/10.1186/s12859-017-1868-5 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lyu, Chen
Chen, Bo
Ren, Yafeng
Ji, Donghong
Long short-term memory RNN for biomedical named entity recognition
title Long short-term memory RNN for biomedical named entity recognition
title_full Long short-term memory RNN for biomedical named entity recognition
title_fullStr Long short-term memory RNN for biomedical named entity recognition
title_full_unstemmed Long short-term memory RNN for biomedical named entity recognition
title_short Long short-term memory RNN for biomedical named entity recognition
title_sort long short-term memory rnn for biomedical named entity recognition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663060/
https://www.ncbi.nlm.nih.gov/pubmed/29084508
http://dx.doi.org/10.1186/s12859-017-1868-5
work_keys_str_mv AT lyuchen longshorttermmemoryrnnforbiomedicalnamedentityrecognition
AT chenbo longshorttermmemoryrnnforbiomedicalnamedentityrecognition
AT renyafeng longshorttermmemoryrnnforbiomedicalnamedentityrecognition
AT jidonghong longshorttermmemoryrnnforbiomedicalnamedentityrecognition