Husni A. Al-Muhtseb

@admissions.kfupm.edu.sa

Information and Computer Science Department
King Fahd University of Petroleum and Minerals

Husni A. Al-Muhtseb


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https://researchid.co/muhtaseb

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Education, Computer Science Applications
22

Scopus Publications

1156

Scholar Citations

18

Scholar h-index

26

Scholar i10-index

Scopus Publications

  • Hypernymy Relation in NLP: Tasks, Approaches, Resources, and Future Directions—A Systematic Literature Review
    Randah Alharbi, Husni Al-Muhtaseb, Tarek Helmy
    IEEE Access, 2025
    Hypernymy is a semantic relation between two terms, where a more specific term is entailed by a more general term—that is, the meaning of the more specific term is encompassed by the meaning of the more general term. This relation is crucial for many natural language processing (NLP) tasks, including textual entailment, search query expansion, and machine translation. In this systematic literature review, the hypernymy relation in the context of NLP is investigated, with a focus on identifying hypernymy-related tasks, employed approaches, available resources, and future research directions. The reviewed studies were extracted from five pre-defined databases, covering the period from 2018 to March 2023. The review process identified 75 primary studies that were analyzed to extract the targeted tasks, languages, approaches, representations, and datasets. The synthesized data were used to address the review questions. The review identifies the main hypernymy-related tasks, including hypernymy extraction, detection, directionality, graded lexical entailment, and discovery. The evaluation practices employed were summarized, including accuracy, F1, Mean Average Precision (MAP), and Spearman’s correlation coefficient. The targeted languages are highlighted, with English being the most studied; however, multilingual coverage is steadily growing. Several benchmark datasets for each task are presented, along with their statistics, characteristics, and construction techniques. Additionally, representation techniques are summarized, ranging from Word2Vec, FastText, and GloVe to hypernymy-specific representations. Finally, research gaps are discussed, and potential future directions are outlined. This review consolidates scattered findings and provides a practical map of tasks, resources, and techniques for researchers building hypernymy-aware NLP systems.
  • BPTI: Bilingual Printed Text Images Dataset for Recognition Purposes
    Mohammed Yahia, Husni Al-Muhtaseb
    International Arab Journal of Information Technology, 2023
    Datasets of text images are important for optical text recognition systems. Such datasets can be used to enhance performance and recognition rates. In this research work, we present a bilingual dataset consists of Arabic/English text images to address the lack of availability of bilingual text databases. The presented dataset consists of 97812 text images, which are categorized into two groups; Scanned page and digitized line images. Images of the two forms are written with 10 fonts and four sizes, and prepared/scanned with four dpi resolutions. The dataset preparation process includes text collection, text editing, image construction, and image processing. The dataset can be used in optical text recognition, optical font recognition, language identification, and segmentation. Different text recognition and language identification experiments have been conducted using images of the dataset and Hidden Markov Model (HMM) classifier. For the digitized images recognition experiments, the best-achieved recognition correctness is 99.01% and the best accuracy is 99.01%. The font that has the highest recognition rates was Tahoma. For the scanned images recognition experiments, Tahoma has also shown the highest performance with 97.86% for correctness and 97.73% for accuracy. For the language identification experiments, Tahoma has shown the performance with 99.98% for word-language identification rate.
  • Sport-fanaticism lexicons for sentiment analysis in Arabic social text
    Mohammed Alqmase, Husni Al-Muhtaseb
    Social Network Analysis and Mining, 2022
  • Arabic Keyphrase Extraction: Enhancing Deep Learning Models with Pre-trained Contextual Embedding and External Features
    Randah Alharbi, Husni Al-Muhtasab
    Wanlp 2022 7th Arabic Natural Language Processing Proceedings of the Workshop, 2022
    Keyphrase extraction is essential to many Information retrieval (IR) and Natural language Processing (NLP) tasks such as summarization and indexing. This study investigates deep learning approaches to Arabic keyphrase extraction. We address the problem as sequence classification and create a Bi-LSTM model to classify each sequence token as either part of the keyphrase or outside of it. We have extracted word embeddings from two pre-trained models, Word2Vec and BERT. Moreover, we have investigated the effect of incorporating linguistic, positional, and statistical features with word embeddings on performance. Our best-performing model has achieved 0.45 F1-score on ArabicKPE dataset when combining linguistic and positional features with BERT embedding.
  • Sports-fanaticism formalism for sentiment analysis in Arabic text
    Mohammed Alqmase, Husni Al-Muhtaseb, Habib Rabaan
    Social Network Analysis and Mining, 2021
  • Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition
    Khalid M. O. Nahar, Mohammed Abu Shquier, Wasfi G. Al-Khatib, Husni Al-Muhtaseb, Moustafa Elshafei
    International Journal of Speech Technology, 2016
  • Arabic Phonemes Transcription Using Learning Vector Quantization: 'Towards the Development of Fast Quranic Text Transcription'
    Khalid M.O. Nahar, Wasfi G. Al-Khatib, Moustafa Elshafei, Husni Al-Muhtaseb, Mansour M. Alghamdi
    Proceedings 2013 Taibah University International Conference on Advances in Information Technology for the Holy Quran and Its Sciences Nooric 2013, 2015
    In this paper, we investigated the use of Learning Vector Quantization (LVQ) for phoneme transcription in Arabic speech recognition systems. We used Arabic speech corpus of TV news clips. Then, we employed feature vectors, which embed the frame neighboring correlation information between adjacent phonemes to replace the traditional trip hones models. Next, we generated the phonemes codebooks using the K-means splitting algorithm. After that, we trained the generated codebooks using the LVQ algorithm. When using the trained LVQ codebooks in utterance phoneme transcription of an open vocabulary test corpus, the phoneme recognition rate was 72% without the use of any added phoneme big rams or HMM models. The results of this research if improved could be used to serve the holy Quran text transcription without any phonemes big rams (phonemes language model). This would increase the speed of the Quranic speech to text transcription and creates the infrastructure of suitable high speed automatic identification system of Quranic sounds recognition and translation.
  • Arabic phonemes transcription using data driven approach
    International Arab Journal of Information Technology, 2015
  • Data-driven Arabic phoneme recognition using varying number of HMM states
    K. M. O. Nahar, W. G. Al-Khatib, M. Elshafei, H. Al-Muhtaseb, M. M. Alghamdi
    2013 1st International Conference on Communications Signal Processing and their Applications Iccspa 2013, 2013
    Continuous Arabic Speech Recognition, appears in many real life applications. Its speed, accuracy and improvement are highly dependent on the accuracy of the language phonemes set. The main goal of this research is to recognize and transcribe the Arabic phonemes based on a data-driven approach. We built a phoneme recognizer based on a data driven approach using HTK tool. Different numbers of Gaussian mixtures with different numbers of HMM states were used in modeling the Arabic phonemes in order to reach the best configuration. The corpus used consists of about 4000 files, representing 5 recorded hours of modern standard Arabic of TV-News. The maximum phoneme recognition accuracy reached was 56.79%. This result is very encouraging and shows the viability of our approach as compared to using a fixed number of HMM states.
  • Statistical analysis of Arabic phonemes used in Arabic speech recognition
    Khalid M. O Nahar, Mustafa Elshafei, Wasfi G. Al-Khatib, Husni Al-Muhtaseb, Mansour M. Alghamdi
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Within-word pronunciation variation modeling for Arabic ASRs: A direct data-driven approach
    Dia AbuZeina, Wasfi Al-Khatib, Moustafa Elshafei, Husni Al-Muhtaseb
    International Journal of Speech Technology, 2012
  • Rescoring N-Best Hypotheses for Arabic Speech Recognition: A Syntax-Mining Approach
    Amta 2012 4th Workshop on Computational Approaches to Arabic Script Based Languages Proceedings, 2012
  • Arabic optical character recognition: Recent trends and future directions
    Husni Al-Muhtaseb, Rami Qahwaji
    Applied Signal and Image Processing Multidisciplinary Advancements, 2011
  • Toward enhanced Arabic speech recognition using part of speech tagging
    Dia AbuZeina, Wasfi Al-Khatib, Moustafa Elshafei, Husni Al-Muhtaseb
    International Journal of Speech Technology, 2011
  • Cross-word Arabic pronunciation variation modeling for speech recognition
    Dia AbuZeina, Wasfi Al-Khatib, Moustafa Elshafei, Husni Al-Muhtaseb
    International Journal of Speech Technology, 2011
  • Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking
    Jawad H AlKhateeb, Jinchang Ren, Jianmin Jiang, Husni Al-Muhtaseb
    Pattern Recognition Letters, 2011
  • Recognition of off-line printed Arabic text using Hidden Markov Models
    Husni A. Al-Muhtaseb, Sabri A. Mahmoud, Rami S. Qahwaji
    Signal Processing, 2008
  • Generation of arabic phonetic dictionaries for speech recognition
    Mohamed Ali, Moustafa Elshafei, Mansour Al-Ghamdi, Husni Al-Muhtaseb, Atef Al-Najjar
    2008 International Conference on Innovations in Information Technology Iit 2008, 2008
  • Arabic broadcast news transcription system
    Mansour Alghamdi, Moustafa Elshafei, Husni Al-Muhtaseb
    International Journal of Speech Technology, 2007
  • Techniques for high quality Arabic speech synthesis
    M Elshafei
    Information Sciences, 2002
  • New fault models and efficient BIST algorithms for dual-port memories
    A.A. Amin, M.Y. Osman, R.E. Abdel-Aal, H. Al-Muhtaseb
    IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, 1997
  • KHABEER: An object-oriented arabic expert system shell
    Arabian Journal for Science and Engineering, 1997

RECENT SCHOLAR PUBLICATIONS

  • ASRD: Development and Validation of a Large-Scale Arabic Semantic Relation Dataset
    R Alharbi, T Helmy, A Al-Saghyir, S Aglan, A Alosaimy, H Al-Muhtaseb
    2025
  • Hypernymy Relation in NLP: Tasks, Approaches, Resources, and Future Directions—A Systematic Literature Review
    R Alharbi, H Al-Muhtaseb, T Helmy
    IEEE Access 13, 206272-206310 , 2025
    2025
    Citations: 1
  • BPTI: Bilingual Printed Text Images Dataset for Recognition Purposes
    M Yahia, H Al-Muhtaseb
    The International Arab Journal of Information Technology 20 (4) , 2023
    2023
  • Arabic keyphrase extraction: Enhancing deep learning models with pre-trained contextual embedding and external features
    R Alharbi, H Al-Muhtasab
    Proceedings of the Seventh Arabic Natural Language Processing Workshop … , 2022
    2022
    Citations: 4
  • Sport-fanaticism lexicons for sentiment analysis in Arabic social text
    M Alqmase, H Al-Muhtaseb
    Social Network Analysis and Mining 12 (1), 56 , 2022
    2022
    Citations: 5
  • BPTI: Bilingual Printed Text Images Dataset for Recognition Purposes
    M Yahia, H Al-Muhtaseb
    Social Science Research Network (SSRN). https://papers.ssrn.com/sol3/papers … , 2022
    2022
  • Sports-fanaticism formalism for sentiment analysis in Arabic text
    M Alqmase, H Al-Muhtaseb, H Rabaan
    Social Network Analysis and Mining 11 (1), 52 , 2021
    2021
    Citations: 26
  • Recognition of Printed Arabic-English Text
    MHN Yahia
    PQDT-Global , 2018
    2018
  • Arabic Dataset for Automatic Keyphrase Extraction
    M Al Logmani, H Al Muhtaseb
    Seventh International Conference on Computer Science and Information … , 2017
    2017
    Citations: 3
  • Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition
    KMO Nahar, M Abu Shquier, WG Al-Khatib, H Al-Muhtaseb, M Elshafei
    International Journal of Speech Technology 19 (3), 495-508 , 2016
    2016
    Citations: 28
  • An Arabic corpus to assist in the automatic extraction of key-phrases (in Arabic) مكنز عربي للمساعدة في الاستنباط الآلي للعبارات المفتاحية‎
    M Al Logmani, H Al-Muhtaseb
    The 5th international conference on Arabic language, Dubai. المؤتمر الدولي … , 2016 ‎
    2016
  • Modeling the phenomenon of changing word pronunciation resulting from intonation judgements (in Arabic) نمذجة ظاهرة تغير نطق الكلمات الناتج عن أحكام التجويد ‎
    M Amro, W Al-Khatib, Elshafei, Moustafa, H Al-Muhtaseb
    The 5th international conference on Arabic language, Dubai. المؤتمر الدولي … , 2016 ‎
    2016
  • Post-processing optimization for Arabic optical character recognition (In Arabic) تحسين مرحلة "بعد المعالجة" في نظام التعرف الضوئي الآلي على الكتابة العربية ‎
    H Al-Muhtaseb, H Luqman
    The 5th international conference on Arabic language, Dubai. المؤتمر الدولي … , 2016 ‎
    2016
  • Automatic vocalization of Arabic text
    YMS Khraishi
    PQDT-Global , 2016
    2016
    Citations: 1
  • Towards A Minimal Phonetic Set for Quran Recitation
    HA Al-Muhtaseb, SA Bellegdi
    International Journal on Islamic an Al-Muhtaseb, HA, & Bellegdi, SA (2016 … , 2016
    2016
    Citations: 1
  • Automatic rule based phonetic transcription and syllabification for quranic text
    SA Bellegdi, HA Al-Muhtaseb
    International Journal on Islamic Applications in Computer Science And … , 2015
    2015
    Citations: 6
  • Arabic Phonemes Transcription using Data Driven Approach.
    K Nahar, H Al-Muhtaseb, W Al-Khatib, M Elshafei, M Alghamdi
    International Arab Journal of Information Technology (IAJIT) 12 (3) , 2015
    2015
    Citations: 19
  • System and method for decoding speech
    DEM Abuzeina, M Elshafei, H Al-Muhtaseb, WG Al-Khatib
    US Patent App. 13/597,162 , 2014
    2014
    Citations: 41
  • Arabic Phonemes Transcription Using Learning Vector Quantization:" Towards the Development of Fast Quranic Text Transcription"
    KMO Nahar, WG Al-Khatib, M Elshafei, H Al-Muhtaseb, MM Alghamdi
    2013 Taibah University International Conference on Advances in Information … , 2013
    2013
    Citations: 2
  • Method of generating a transliteration font
    S Awaida, H Al-Muhtaseb
    US Patent 8,438,008 , 2013
    2013
    Citations: 16

MOST CITED SCHOLAR PUBLICATIONS

  • Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking
    JH AlKhateeb, J Ren, J Jiang, H Al-Muhtaseb
    Pattern Recognition Letters 32 (8), 1081-1088 , 2011
    2011
    Citations: 175
  • Recognition of off-line printed Arabic text using Hidden Markov Models
    HA Al-Muhtaseb, SA Mahmoud, RS Qahwaji
    Signal processing 88 (12), 2902-2912 , 2008
    2008
    Citations: 132
  • Statistical methods for automatic diacritization of Arabic text
    M Elshafei, H Al-Muhtaseb, M Alghamdi
    The Saudi 18th National Computer Conference. Riyadh 18, 301-306 , 2006
    2006
    Citations: 102
  • Techniques for high quality Arabic speech synthesis
    M Elshafei, H Al-Muhtaseb, M Al-Ghamdi
    Information sciences 140 (3-4), 255-267 , 2002
    2002
    Citations: 81
  • Arabic broadcast news transcription system
    M Alghamdi, M Elshafei, H Al-Muhtaseb
    International Journal of Speech Technology 10, 183-195 , 2009
    2009
    Citations: 60
  • Generation of Arabic phonetic dictionaries for speech recognition
    M Ali, M Elshafei, M Al-Ghamdi, H Al-Muhtaseb, A Al-Najjar
    2008 International conference on innovations in information technology, 59-63 , 2008
    2008
    Citations: 42
  • System and method for decoding speech
    DEM Abuzeina, M Elshafei, H Al-Muhtaseb, WG Al-Khatib
    US Patent App. 13/597,162 , 2014
    2014
    Citations: 41
  • Machine generation of Arabic diacritical marks
    MA Elshafei
    2006
    Citations: 40
  • Arabic phonetic dictionaries for speech recognition
    M Ali, M Elshafei, M Al-Ghamdi, H Al-Muhtaseb
    Journal of Information Technology Research (JITR) 2 (4), 67-80 , 2009
    2009
    Citations: 37
  • Cross-word Arabic pronunciation variation modeling for speech recognition
    D AbuZeina, W Al-Khatib, M Elshafei, H Al-Muhtaseb
    International Journal of Speech Technology 14 (3), 227-236 , 2011
    2011
    Citations: 34
  • Automatic arabic text image optical character recognition method
    HA Al-Muhtaseb, SA Mahmoud, R Qahwaji
    US Patent 8,150,160 , 2012
    2012
    Citations: 31
  • Statistical analysis of Arabic phonemes used in Arabic speech recognition
    KMO Nahar, M Elshafei, WG Al-Khatib, H Al-Muhtaseb, MM Alghamdi
    Neural Information Processing: 19th International Conference, ICONIP 2012 … , 2012
    2012
    Citations: 29
  • Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition
    KMO Nahar, M Abu Shquier, WG Al-Khatib, H Al-Muhtaseb, M Elshafei
    International Journal of Speech Technology 19 (3), 495-508 , 2016
    2016
    Citations: 28
  • Sports-fanaticism formalism for sentiment analysis in Arabic text
    M Alqmase, H Al-Muhtaseb, H Rabaan
    Social Network Analysis and Mining 11 (1), 52 , 2021
    2021
    Citations: 26
  • Some Differences Between Arabic and English: A Step Towards an Arabic Upper Model
    H Al-Muhtaseb, C Mellish
    The 6th International Conference on Multilingual Computing, Cambridge, UK. , 1998
    1998
    Citations: 22
  • Techniques for high quality Arabic speech synthesis
    H Al-Muhtaseb, M Elshafei, M Al-Ghamdi
    Information sciences 140, 255-267 , 2002
    2002
    Citations: 21
  • Speaker-independent natural Arabic speech recognition system
    M Elshafei, H Al-Muhtaseb, M Al-Ghamdi
    The International Conference on Intelligent Systems , 2008
    2008
    Citations: 20
  • Arabic Phonemes Transcription using Data Driven Approach.
    K Nahar, H Al-Muhtaseb, W Al-Khatib, M Elshafei, M Alghamdi
    International Arab Journal of Information Technology (IAJIT) 12 (3) , 2015
    2015
    Citations: 19
  • Within-word pronunciation variation modeling for Arabic ASRs: a direct data-driven approach
    D AbuZeina, W Al-Khatib, M Elshafei, H Al-Muhtaseb
    International Journal of Speech Technology 15 (2), 65-75 , 2012
    2012
    Citations: 18
  • Toward enhanced Arabic speech recognition using part of speech tagging
    D AbuZeina, W Al-Khatib, M Elshafei, H Al-Muhtaseb
    International Journal of Speech Technology 14 (4), 419-426 , 2011
    2011
    Citations: 18