Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm

Aliya Kalykulova; Bilal Saoud; Ibraheem Shayea; Dauren Sagidullauly

doi:10.1007/978-3-032-00232-7_24

Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm

Aliya Kalykulova
, Bilal Saoud^*
, Ibraheem Shayea
, Dauren Sagidullauly

^*Bu çalışma için yazışmadan sorumlu yazar

Elektrik-Elektronik Fakültesi

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

Effective extraction of domain-specific terms and named entities is a key challenge in text mining. This paper investigates the use of the k-means clustering algorithm for unsupervised extraction of unigrams and named entities from text data. The approach groups terms based on their vector representations, enabling the identification of semantically similar words without labeled data. Experiments conducted on the ACTER (Annotated Corpora for Term Extraction Research) corpus evaluate the method using precision, recall, and F1-score. Results show average scores of 25.79% precision, 40.05% recall and 30.47% F1-score, with optimal performance achieved using 40 to 60 clusters. Future work will explore algorithm optimization and comparisons with alternative extraction techniques.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations
Editörler	Ali Othman Albaji
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	375-386
Sayfa sayısı	12
ISBN (Basılı)	9783032002310
DOI'lar	https://doi.org/10.1007/978-3-032-00232-7_24
Yayın durumu	Yayınlandı - 2026
Etkinlik	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 - Tripoli, Libya Süre: 9 Tem 2025 → 10 Tem 2025

Yayın serisi

Adı	Studies in Computational Intelligence
Hacim	1229 SCI
ISSN (Basılı)	1860-949X
ISSN (Elektronik)	1860-9503

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025
Ülke/Bölge	Libya
Şehir	Tripoli
Periyot	9/07/25 → 10/07/25

Bibliyografik not

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Belgeye Erişim

10.1007/978-3-032-00232-7_24

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Kalykulova, A., Saoud, B., Shayea, I., & Sagidullauly, D. (2026). Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm. In A. O. Albaji (Ed.), Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations (pp. 375-386). (Studies in Computational Intelligence; Hac. 1229 SCI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-00232-7_24

Kalykulova, Aliya ; Saoud, Bilal ; Shayea, Ibraheem et al. / Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm. Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. editör / Ali Othman Albaji. Springer Science and Business Media Deutschland GmbH, 2026. pp. 375-386 (Studies in Computational Intelligence).

@inproceedings{060c98b61f934342b3cb65a650e0e563,

title = "Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm",

abstract = "Effective extraction of domain-specific terms and named entities is a key challenge in text mining. This paper investigates the use of the k-means clustering algorithm for unsupervised extraction of unigrams and named entities from text data. The approach groups terms based on their vector representations, enabling the identification of semantically similar words without labeled data. Experiments conducted on the ACTER (Annotated Corpora for Term Extraction Research) corpus evaluate the method using precision, recall, and F1-score. Results show average scores of 25.79\% precision, 40.05\% recall and 30.47\% F1-score, with optimal performance achieved using 40 to 60 clusters. Future work will explore algorithm optimization and comparisons with alternative extraction techniques.",

keywords = "Automated term extraction, Clustering, NLP, Unigrams, Unsupervised annotator, k-means",

author = "Aliya Kalykulova and Bilal Saoud and Ibraheem Shayea and Dauren Sagidullauly",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025 ; Conference date: 09-07-2025 Through 10-07-2025",

year = "2026",

doi = "10.1007/978-3-032-00232-7\_24",

language = "English",

isbn = "9783032002310",

series = "Studies in Computational Intelligence",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "375--386",

editor = "Albaji, \{Ali Othman\}",

booktitle = "Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations",

address = "Germany",

}

Kalykulova, A, Saoud, B, Shayea, I & Sagidullauly, D 2026, Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm. in AO Albaji (ed.), Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. Studies in Computational Intelligence, hac. 1229 SCI, Springer Science and Business Media Deutschland GmbH, pp. 375-386, International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025, Tripoli, Libya, 9/07/25. https://doi.org/10.1007/978-3-032-00232-7_24

Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm. / Kalykulova, Aliya; Saoud, Bilal; Shayea, Ibraheem et al.
Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. ed. / Ali Othman Albaji. Springer Science and Business Media Deutschland GmbH, 2026. p. 375-386 (Studies in Computational Intelligence; Hac. 1229 SCI).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm

AU - Kalykulova, Aliya

AU - Saoud, Bilal

AU - Shayea, Ibraheem

AU - Sagidullauly, Dauren

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - Effective extraction of domain-specific terms and named entities is a key challenge in text mining. This paper investigates the use of the k-means clustering algorithm for unsupervised extraction of unigrams and named entities from text data. The approach groups terms based on their vector representations, enabling the identification of semantically similar words without labeled data. Experiments conducted on the ACTER (Annotated Corpora for Term Extraction Research) corpus evaluate the method using precision, recall, and F1-score. Results show average scores of 25.79% precision, 40.05% recall and 30.47% F1-score, with optimal performance achieved using 40 to 60 clusters. Future work will explore algorithm optimization and comparisons with alternative extraction techniques.

AB - Effective extraction of domain-specific terms and named entities is a key challenge in text mining. This paper investigates the use of the k-means clustering algorithm for unsupervised extraction of unigrams and named entities from text data. The approach groups terms based on their vector representations, enabling the identification of semantically similar words without labeled data. Experiments conducted on the ACTER (Annotated Corpora for Term Extraction Research) corpus evaluate the method using precision, recall, and F1-score. Results show average scores of 25.79% precision, 40.05% recall and 30.47% F1-score, with optimal performance achieved using 40 to 60 clusters. Future work will explore algorithm optimization and comparisons with alternative extraction techniques.

KW - Automated term extraction

KW - Clustering

KW - NLP

KW - Unigrams

KW - Unsupervised annotator

KW - k-means

UR - https://www.scopus.com/pages/publications/105028287079

U2 - 10.1007/978-3-032-00232-7_24

DO - 10.1007/978-3-032-00232-7_24

M3 - Conference contribution

AN - SCOPUS:105028287079

SN - 9783032002310

T3 - Studies in Computational Intelligence

SP - 375

EP - 386

BT - Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations

A2 - Albaji, Ali Othman

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on AI: Current Research, Industry Trends, and Innovations, FICAILY 2025

Y2 - 9 July 2025 through 10 July 2025

ER -

Kalykulova A, Saoud B, Shayea I, Sagidullauly D. Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm. In Albaji AO, editör, Selected Papers from the International Conference on Artificial Intelligence - FICAILY2025 - Current Research, Industry Trends, and Innovations. Springer Science and Business Media Deutschland GmbH. 2026. p. 375-386. (Studies in Computational Intelligence). doi: 10.1007/978-3-032-00232-7_24

Automatic Unsupervised Extraction of Unigrams of Terms and Named Entities Using the K-Means Clustering Algorithm

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Belgeye Erişim

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