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Using a Hybrid Approach for Entity Recognition in the Biomedical Domain

Marco Basaldella, Lenz Furrer, Nicola Colic, Tilia Renate Ellendorff, Carlo Tasso, Fabio Rinaldi
This paper presents an approach towards high performance extraction of biomedical entities from the literature, which is built by combining a high recall dictionarybased technique with a high-precision  ...  The technique is then evaluated on the CRAFT corpus. We present the performance we obtained, analyze the errors and propose a possible follow-up of this work.  ...  Conclusions and Future Work In this paper we have presented and evaluated an approach towards efficient recognition of biomedical entities in the scientific literature.  ... 
doi:10.5167/uzh-125712 fatcat:ak75ku4m6bdkngwm7hapjw5dtq

Biomedical Named Entity Recognition - a swift review

S. Vijaya
2017 International Journal Of Engineering And Computer Science  
This paper analyses various methods used for NER particularly in the field of Biomedical domain.  ...  The main focus of this paper is taking a swift review on the Biomedical Named Entity Recognition which is the most complex task in Information Extraction.  ...  This motivated us to review various methods used for NER in Biomedical domain, analyse the performance and propose an efficient method to extract biomedical entities effectively.  ... 
doi:10.18535/ijecs/v6i5.57 fatcat:upi7t3tbg5hllav5g2e57qgd2a

A hybrid named entity tagger for tagging human proteins/genes

Kalpana Raja, Suresh Subramani, Jeyakumar Natarajan
2014 International Journal of Data Mining and Bioinformatics  
Though many taggers are available for this Named Entity Recognition (NER) task, we found none of them achieve a good state-of-art tagging for human genes/proteins.  ...  The predominant step and pre-requisite in the analysis of scientific literature is the extraction of gene/protein names in biomedical texts.  ...  Li et al. (2009) used two-phase CRF approach to identify biomedical entities, in which the recognition task is divided into two subtasks: Named Entity Detection (NED) and Named Entity Classification (  ... 
doi:10.1504/ijdmb.2014.064545 pmid:25946866 fatcat:36zzd5gionet3feejsaf7onwqm

ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition

Nada Boudjellal, Huaping Zhang, Asif Khan, Arshad Ahmad, Rashid Naseem, Jianyun Shang, Lin Dai, Atif Khan
2021 Complexity  
Being a widely used language globally, English is taking over most of the research conducted in this field, especially in the biomedical domain.  ...  This work presents a BERT-based model to identify biomedical named entities in the Arabic text data (specifically disease and treatment named entities) that investigates the effectiveness of pretraining  ...  used to mine the Arabic scripts, especially in the biomedical domain. e biomedical domain has a special and complex structure for named entities as compared to other open text domains.  ... 
doi:10.1155/2021/6633213 fatcat:ayz63nq2n5cyjl7hdwufzs45w4

BERN2: an advanced neural biomedical named entity recognition and normalization tool [article]

Mujeen Sung, Minbyul Jeong, Yonghwa Choi, Donghyeon Kim, Jinhyuk Lee, Jaewoo Kang
2022 arXiv   pre-print
In this paper, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool (Kim et al., 2019) by employing a multi-task  ...  In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g., diseases  ...  See Supplementary data for the performance of our hybrid NEN models. Use Cases of BERN2 We introduce two use cases in which BERN was used in the field of biomedical information extraction.  ... 
arXiv:2201.02080v2 fatcat:sthxajdowfa6lp2w4k6co3l2gq

A Hybrid Method of Linguistic Features and Clustering Approach for Identifying Biomedical Named Entities

E. Alharbi, S. Tiun
2015 Asian Journal of Applied Sciences  
Named entity is a term that has been widely used in the field of Natural Language Processing (NLP). It contains the names of persons, organizations, locations, dates and currencies.  ...  Biomedical Named Entity Recognition (BNER) is one of the fields that contains variety of named entities such as genes, DNA, RNA, chemical compounds.  ...  For instance, Friedrich et al. (2006) have proposed several machine learning techniques with a dictionary-based approach in terms of identifying biomedical entities using the benchmark settings of the  ... 
doi:10.3923/ajaps.2015.210.216 fatcat:3cxngu6sc5fujix27hu6dbksxm

BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition

Gurusamy Murugesan, Sabenabanu Abdulkadhar, Balu Bhasuran, Jeyakumar Natarajan
2017 EURASIP Journal on Bioinformatics and Systems Biology  
In this paper, we describe our hybrid named entity tagging approach namely BCC-NER (bidirectional, contextual clues named entity tagger for gene/protein mention recognition).  ...  Tagging biomedical entities such as gene, protein, cell, and cell-line is the first step and an important pre-requisite in biomedical literature mining.  ...  Discussions In this paper, we describe our hybrid named entity recognition system named BCC-NER for tagging biomedical entities.  ... 
doi:10.1186/s13637-017-0060-6 pmid:28477208 pmcid:PMC5419958 fatcat:7m6vr5zqbvcedlos4yf2v32tfm

Application of Biomedical Text Mining [chapter]

Lejun Gong
2018 Artificial Intelligence - Emerging Trends and Applications  
The aim is to provide a way to quickly understand biomedical text mining for some researchers.  ...  Using the information will help understand the mechanism of disease generation, promote the development of disease diagnosis technology, and promote the development of new drugs in the field of biomedical  ...  The mentioned three approaches have their own advantages, respectively. There is also a hybrid approach to be used for identifying biomedical entities.  ... 
doi:10.5772/intechopen.75924 fatcat:5o27ptssi5fwzbelnpktkwplby

OGER++: hybrid multi-type entity recognition

Lenz Furrer, Anna Jancso, Nicola Colic, Fabio Rinaldi
2019 Journal of Cheminformatics  
We present a text-mining tool for recognizing biomedical entities in scientific literature.  ...  OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component.  ...  Additional thanks to the reviewers for their helpful suggestions.  ... 
doi:10.1186/s13321-018-0326-3 pmid:30666476 pmcid:PMC6689863 fatcat:mwhlaw5aqrhphbda7ze3dx6f5e

Improving Named Entity Recognition for Biomedical and Patent Data Using Bi-LSTM Deep Neural Network Models [chapter]

Farag Saad, Hidir Aras, René Hackl-Sommer
2020 Lecture Notes in Computer Science  
The daily exponential increase of biomedical information in scientific literature and patents is a main obstacle to foster advances in biomedical research.  ...  A fundamental step hereby is to find key information (named entities) inside these publications applying Biomedical Named Entities Recognition (BNER).  ...  In the biomedical domain, for example with regard to the hybrid approach, a two-fold method for Biomedical NER was proposed in which dictionary-based NER was combined with corpus-based disambiguation  ... 
doi:10.1007/978-3-030-51310-8_3 fatcat:d6larpsk45h3bbdxojpdo74phi

BIOKDD 2005 workshop report

Srinivasan Parthasarathy, Wei Wang, Mohammed Zaki
2005 SIGKDD Explorations  
the best algorithms for biomedical named-entity recognition and those for general newswire named-entity recognition.  ...  In order to take advantage of the rich feature representations and external domain knowledge used by different systems, Lou Si, Tapas Kanungo, and Xiangji Huang proposed a collection of biomedical named-entity  ... 
doi:10.1145/1117454.1117472 fatcat:6mbi5aabrneslek5seugdpipey

Biomedical Named Entity Recognition Using the SVM Methodologies and bio Tagging Schemes

Thiyagu Meenachisundaram, Manjula Dhanabalachandran
2020 Revista de chimie (Bucuresti)  
Biomedical Named Entity Recognition (BNER) is identification of entities such as drugs, genes, and chemicals from biomedical text, which help in information extraction from the domain literature.  ...  It uses the sequence tagging capability of CRF to identify the boundary of the entity and classification efficiency of SVM to detect subtypes in BNER.  ...  In case of biomedical entities in GENIA corpus, in detecting entity tag for unseen word the context of the word plays a vital role.  ... 
doi:10.37358/rc.21.4.8456 fatcat:uclcz7smbvdx5cnrc6qkiq2tae

Learning adaptive representations for entity recognition in the biomedical domain

Ivano Lauriola, Fabio Aiolli, Alberto Lavelli, Fabio Rinaldi
2021 Journal of Biomedical Semantics  
To this end, we use a hybrid architecture for biomedical entity recognition which integrates dictionary look-up (also known as gazetteers) with machine learning techniques.  ...  Background Named Entity Recognition is a common task in Natural Language Processing applications, whose purpose is to recognize named entities in textual documents.  ...  Additionally, in the biomedical domain, affixes usually have a specific meaning, and they could have useful information to recognize relevant entities.  ... 
doi:10.1186/s13326-021-00238-0 pmid:34001263 fatcat:bw76dwrw3rcfxjj676mw5rdg2y

Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature

Miguel García-Remesal, Alejandro García-Ruiz, David Pérez-Rey, Diana de la Iglesia, Víctor Maojo
2013 BioMed Research International  
These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches  ...  For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature.  ...  e authors would also like to thank Professor Casimir A. Kulikowski, Dr. Martin Fritts, and Dr. Raul E. Cachau for their useful suggestions and comments.  ... 
doi:10.1155/2013/410294 pmid:23509721 pmcid:PMC3591181 fatcat:2wiewurvpvbqjmwwrr7nplijey

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research  ...  hybrid system of recognizing composite biomedical named entities [110]  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi
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