LSTMVoter : chemical named entity recognition using a conglomerate of sequence labeling tools

  • Background: Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier. Results: We introduce LSTMVoter, a bidirectional long short-term memory (LSTM) tagger that utilizes a conditional random field layer in conjunction with attention-based feature modeling. Our approach explores information about features that is modeled by means of an attention mechanism. LSTMVoter outperforms each extractor integrated by it in a series of experiments. On the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus, LSTMVoter achieves an F1-score of 90.04%; on the BioCreative V.5 chemical entity mention in patents corpus, it achieves an F1-score of 89.01%. Availability and implementation: Data and code are available at https://github.com/texttechnologylab/LSTMVoter.

Download full text files

Export metadata

Metadaten
Author:Wahed Hemati, Alexander MehlerORCiDGND
URN:urn:nbn:de:hebis:30:3-486148
DOI:https://doi.org/10.1186/s13321-018-0327-2
ISSN:1758-2946
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/30631966
Parent Title (English):Journal of cheminformatics
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
Year of Completion:2019
Date of first Publication:2019/01/10
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2019/01/15
Tag:Attention mechanism; BioCreative V.5; BioNLP; CEMP; CHEMDNER; Deep learning; LSTM; Named entity recognition
Volume:11
Issue:1, Art. 3
Page Number:7
First Page:1
Last Page:7
Note:
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.
HeBIS-PPN:446487902
Institutes:Informatik und Mathematik / Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Sammlungen:Universit├Ątspublikationen
Open-Access-Publikationsfonds:Informatik und Mathematik
Licence (German):License LogoCreative Commons - Namensnennung 4.0