Named entity recognition for Thai historical data

Name: Nasith Laosen

Organization : Phuket Rajabhat University. Faculty of Science and Technology

Email : nasith.l@pkru.ac.th

Name: Kanjana Laosen

Organization : Prince of Songkla University. College of Computing

Email : kanjana.l@phuket.psu.ac.th

Name: Thummarat Paklao

Organization : Chulalongkorn University. Faculty of Science

Email : 6373005223@student.chula.ac.th

keyword: Named entity recognition

LCSH: Natural language processing (Computer science)

; Historical records

LCSH: PLNLP (Computer program language)

LCSH: Thai language -- Data processing

Abstract: Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), enabling various advanced NLP applications like information extraction, question answering, and text summarization. Thai NER presents unique challenges due to the absence of capitalization, explicit word boundaries, and sentence-ending punctuation. While NER on general domain Thai datasets has been explored, its application to historical text remains an underexplored area. Historical texts contain specialized terminology, necessitating domain-specific NER solutions for optimal performance. This study investigates Thai historical NER, utilizing historical textual data collected from the Wikipedia website. Our goal is to identify suitable word segmentation and NER methods. We evaluate the performance of the Attacut word segmentation algorithm against Deepcut and Newmm. Our findings demonstrate Attacut’s superiority, achiev- ing an F1-score of 0.9557. Furthermore, our proposed SBC model (Sentence Transformer + BiLSTM + CRF) outperforms pre- trained LLMs (BERT-th1, XLM-R, WangchanBERTa) in NER, achieving an average F1-score of 0.97. The overall performance of the Attacut algorithm and the SBC model highlights their suitability for developing advanced NLP applications within the Thai historical domain.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2024

Modified: 2025-06-06

Issued: 2025-06-06

บทความ/Article

application/pdf

BibliograpyCitation : In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.528-533). Phuket : Prince of Songkla University

eng

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