Automatic Information Extraction Technologies From Textual Data
Keywords:
text analytics, information technology, NLP, NER, machine learning, deep learning, natural language processing, BERT, transformer, digital technologies, educational systems.Abstract
This article provides a comprehensive review of technologies for automatic information extraction from textual data. Methods based on natural language processing (NLP), machine learning and deep learning approaches are analyzed in detail. Basic techniques such as Named Entity Recognition (NER), relationship extraction and data description are studied from the point of view of their effectiveness and areas of application. The article presents a comparative analysis of methods based on rules, statistics and neural networks. The results obtained serve to expand automation in modern information systems and improve the quality of education.
References
Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805.
Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), 3-26.
Manning, C.D., & Schutze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
Kodirov, F.E., Axmatova, S.Z. (2019). LiFi-NEW NETWORK TECHNOLOGIES. Nauka i innovatsii v XXI veke: aktual'nye voprosy, otkrytiya i dostizheniya.
Qodirov, F., Allanazarova, A. (2025). Ta'limni boshqarish tizimlari tasnifi. Central Asian Journal of Multidisciplinary Research and Management Studies, 2(11), 113-117.
Qodirov, F. (2020). Masofaviy ta'limda o'qishning qulayliklari va kamchiliklari. Muhammad al-Xorazmiy nomidagi TATU Qarshi filiali.
Qodirov, F.E., Akbarova, D.A., Shokirov, S.H. (2021). Software for working with computer graphics and their tasks. Application of digital image processing fields, 57-58
Qodirov, F., Sa'dullayeva, M. (2025). Virtual reallik (VR) va kengaytirilgan reallik (AR). Molodye uchenye, 3(8), 139-144.
Lafferty, J., McCallum, A., & Pereira, F.C. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML.
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
