Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia

• URL: http://arxiv.org/abs/2304.02746v1
• Date: Tue, 28 Mar 2023 02:31:47 GMT
• Title: Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia
• Authors: Mukhlis Amien
• Abstract summary: Review focuses on the basic technologies, methods, and practical applications that have been developed. Describes developments in basic NLP technologies such as stemming, part-of-speech tagging, and related methods. Describes practical applications in cross-language information retrieval systems, information extraction, and sentiment analysis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemming, part-of-speech tagging, and related methods; practical applications in cross-language information retrieval systems, information extraction, and sentiment analysis; and methods and techniques used in Indonesian language NLP research, such as machine learning, statistics-based machine translation, and conflict-based approaches. This study also explores the application of NLP in Indonesian language industry and research and identifies challenges and opportunities in Indonesian language NLP research and development. Recommendations for future Indonesian language NLP research and development include developing more efficient methods and technologies, expanding NLP applications, increasing sustainability, further research into the potential of NLP, and promoting interdisciplinary collaboration. It is hoped that this review will help researchers, practitioners, and the government to understand the development of Indonesian language NLP and identify opportunities for further research and development.

Related papers

• Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art [70.1063219524999]
  The surge of interest in culturally aware and adapted Natural Language Processing has inspired much recent research. The lack of common understanding of the concept of "culture" has made it difficult to evaluate progress in this emerging area. We propose an extensive taxonomy of elements of culture that can provide a systematic framework for analyzing and understanding research progress.
  arXiv  Detail & Related papers  (2024-06-06T10:16:43Z)
• Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends [26.90343340881045]
  We review recent advances in NLP with the focus on solving problems relevant to the education domain. We present a taxonomy of NLP in the education domain and highlight typical NLP applications including question answering, question construction, automated assessment, and error correction. We conclude with six promising directions for future research, including more datasets in education domain, controllable usage of LLMs, intervention of difficulty-level control, interpretable educational NLP, methods with adaptive learning, and integrated systems for education.
  arXiv  Detail & Related papers  (2024-01-15T07:48:42Z)
• A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis [4.040584701067227]
  Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities.
  arXiv  Detail & Related papers  (2023-01-20T23:38:58Z)
• An Inclusive Notion of Text [69.36678873492373]
  We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
  arXiv  Detail & Related papers  (2022-11-10T14:26:43Z)
• Meta Learning for Natural Language Processing: A Survey [88.58260839196019]
  Deep learning has been the mainstream technique in natural language processing (NLP) area. Deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning studying approaches to learn better algorithms.
  arXiv  Detail & Related papers  (2022-05-03T13:58:38Z)
• Systematic Inequalities in Language Technology Performance across the World's Languages [94.65681336393425]
  We introduce a framework for estimating the global utility of language technologies. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies and more linguistic NLP tasks.
  arXiv  Detail & Related papers  (2021-10-13T14:03:07Z)
• Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19 [58.720142291102135]
  We discuss ways in which current and future NLP approaches can be made more inclusive by covering low-resource languages. We suggest several future directions for researchers interested in maximizing the positive societal impacts of NLP.
  arXiv  Detail & Related papers  (2021-08-11T12:54:26Z)
• FedNLP: A Research Platform for Federated Learning in Natural Language Processing [55.01246123092445]
  We present the FedNLP, a research platform for federated learning in NLP. FedNLP supports various popular task formulations in NLP such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling. Preliminary experiments with FedNLP reveal that there exists a large performance gap between learning on decentralized and centralized datasets.
  arXiv  Detail & Related papers  (2021-04-18T11:04:49Z)
• Deep Learning for Political Science [0.0]
  New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories. Political science has traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. This chapter offers an introduction to such methods drawing examples from political science.
  arXiv  Detail & Related papers  (2020-05-13T19:14:37Z)
• Natural Language Processing Advancements By Deep Learning: A Survey [0.755972004983746]
  This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas.
  arXiv  Detail & Related papers  (2020-03-02T21:32:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.