=Paper=
{{Paper
|id=Vol-3603/Tutorial1
|storemode=property
|title=Natural Language Processing Tutorial for Biomedical Text Mining
|pdfUrl=https://ceur-ws.org/Vol-3603/Tutorial1.pdf
|volume=Vol-3603
|authors=Senay Kafkas,Sumyyah Toonsi,Sakhaa Alsaedi
|dblpUrl=https://dblp.org/rec/conf/icbo/KafkasTA23
}}
==Natural Language Processing Tutorial for Biomedical Text Mining==
https://ceur-ws.org/Vol-3603/Tutorial1.pdf
Natural Language Processing Tutorial for Biomedical
Text Mining - Abstract
Şenay Kafkas1,2,* , Sumyyah Toonsi1,2 and Sakhaa Alsaedi1,2
1
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) Division, King Abdullah University of Science
and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia
2
Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal,
23955, Kingdom of Saudi Arabia
Abstract
In this tutorial, we introduce Natural Language Processing (NLP) for text mining (TM) in the biomedical
domain. The tutorial is structured such that each main concept is backed by hands-on exercises. We
start by introducing the difference between NLP and TM. Then continue with motivating the need
for text mining in the biomedical domain. Next, we introduce basic concepts of NLP tasks such as
Named Entity Recognition (NER), Named Entity Normalization (NEN), and Relationship Extraction
(RE. We also cover in detail the widely used methods being used to implement these tasks. These
methods include dictionary/ontology-based, rule-based, and advanced machine/deep learning-based
approaches. In particular, we cover language models like Word2Vec and transformers (e.g. BERT) and
their applications. Furthermore, we discuss the recent advancements in NLP by focusing on the large
language models covering GPT, ChatGPT, and others. We conclude our tutorial by discussing limitations
and ethics in NLP where we cover the best practices to develop state-of-the-art NLP and TM tools.
The learning objectives of this tutorial are:
• Differences between NLP and TM
• The need for biomedical TM
• Implementation of fundamental NLP tasks: NER, NEN and RE
• Current and future trends in NLP
• Limitations and ethics in NLP and biomedical TM
The learning outcomes of this tutorial are:
• Familiarity with current NLP techniques/tools being used for biomedical TM
• Basic skills to use and develop fundamental NLP tools such as NER and RE
• Familiarity with the current as well as expected future trends in NLP
• Familiarity with the ethics and limitations of biomedical TM
Materials of this tutorial are available from https://github.com/stoonsi/
ICBO-NLP-for-Biomedical-Text-Mining-tutorial/tree/main
Keywords
Natural Language Processing, Text mining
Proceedings of the International Conference on Biomedical Ontologies 2023, August 28th-September 1st, 2023, Brasilia,
Brazil
*
Corresponding author.
$ senay.kafkas@kaust.edu.sa (Ş. Kafkas); sumyyah.toonsi@kaust.edu.sa (S. Toonsi); sakhaa.alsaedi@kaust.edu.sa
(S. Alsaedi)
0000-0001-7509-5786 (Ş. Kafkas); 0000-0003-4746-4649 (S. Toonsi); 0000-0001-7142-8715 (S. Alsaedi)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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