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Journal articles on the topic 'Natural language processing (Computer science)'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Geetha, Dr V., Dr C. K. Gomathy, Mr D. Sri Datta Vallab Yaratha Yagn, and Sai Praneesh. "THE ROLE OF NATURAL LANGUAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27094.

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Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-based systems to contemporary deep learning models. Natural Language Processing is a subfield of computer science and artificial intelligence that deals with the interactions between computers and humans using natural language. It focuses on the ability of computers to understand, interpret, and generate human language. Keywords Natural Language Processing, Text Analysis , Text Mining , Speech Recognition.

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2

Wilks, Yorick. "Natural language processing." Communications of the ACM 39, no. 1 (January 1996): 60–62. http://dx.doi.org/10.1145/234173.234180.

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3

Selfridge, Mallory. "Natural language processing." Artificial Intelligence in Engineering 2, no. 1 (January 1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90076-8.

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Prema, M., Dr V. Raju, and M. Ramya. "Natural Language Processing for Data Science Workforce Analysis." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 13, no. 4 (December 30, 2022): 225–32. http://dx.doi.org/10.58346/jowua.2022.i4.015.

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As the demand for people with Data Science and Data Analysis skills are rising at a very high rate, periodic exploration of the skill sets for jobs in these fields have become essential. This research presents the use of Natural Language Processing for Human Resource Management. It presents the application of such techniques and tools as Python Libraries with Beautiful Soup and Selenium, Web Scrapping, Topic Analysis, Sentiment Analysis, and Natural Language Processing in the identification of skill sets related to Data Scientist, Data Analyst and Data Engineer.

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Cunliffe, Daniel, Andreas Vlachidis, Daniel Williams, and Douglas Tudhope. "Natural language processing for under-resourced languages: Developing a Welsh natural language toolkit." Computer Speech & Language 72 (March 2022): 101311. http://dx.doi.org/10.1016/j.csl.2021.101311.

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6

Zheng, Haotian, Kangming Xu, Huiming Zhou, Yufu Wang, and Guangze Su. "Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis." Academic Journal of Science and Technology 10, no. 1 (March 26, 2024): 62–68. http://dx.doi.org/10.54097/v160aa61.

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Natural Language Processing (NLP) is an interdisciplinary field of computer science, artificial intelligence, and linguistics that focuses on the ability of computers to understand, process, generate, and simulate human language in order to achieve the ability to have natural conversations with humans. The underlying principles of natural language processing are at multiple levels, including linguistics, computer science, and statistics. It involves the study of language structure, semantics, grammar and pragmatics, as well as the statistical analysis and modeling of large-scale corpora. In the process of concrete implementation, it is necessary to process natural language at multiple levels. Based on this, this paper combined deep learning and natural language processing technology to conduct sentiment analysis on patients' comments, so as to recommend drugs that are more suitable for patients, thus achieving accurate drug prescribing and personalized recommendation.

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7

Heidorn, P. Bryan. "Natural language processing." Information Processing & Management 32, no. 1 (January 1996): 122–23. http://dx.doi.org/10.1016/s0306-4573(96)90089-8.

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8

Søgaard, Anders. "Explainable Natural Language Processing." Synthesis Lectures on Human Language Technologies 14, no. 3 (September 21, 2021): 1–123. http://dx.doi.org/10.2200/s01118ed1v01y202107hlt051.

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Thessen, Anne E., Hong Cui, and Dmitry Mozzherin. "Applications of Natural Language Processing in Biodiversity Science." Advances in Bioinformatics 2012 (May 22, 2012): 1–17. http://dx.doi.org/10.1155/2012/391574.

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Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.

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10

King, Margaret. "Evaluating natural language processing systems." Communications of the ACM 39, no. 1 (January 1996): 73–79. http://dx.doi.org/10.1145/234173.234208.

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11

Barnett, Jim, Kevin Knight, Inderjeet Mani, and Elaine Rich. "Knowledge and natural language processing." Communications of the ACM 33, no. 8 (August 1990): 50–71. http://dx.doi.org/10.1145/79173.79177.

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12

Belov, Serey, Daria Zrelova, Petr Zrelov, and Vladimir Korenkov. "Overview of methods for automatic natural language text processing." System Analysis in Science and Education, no. 3 (2020) (September 30, 2020): 8–22. http://dx.doi.org/10.37005/2071-9612-2020-3-8-22.

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This paper provides a brief overview of modern methods and approaches used for automatic processing of text information. In English-language literature, this area of science is called NLP-Natural Language Processing. The very name suggests that the subject of analysis (and for many tasks – and synthesis) are materials presented in one of the natural languages (and for a number of tasks – in several languages simultaneously), i.e. national languages of communication between people. Programming languages are not included in this group. In Russian-language literature, this area is called Computer (or mathematical) linguistics. NLP (computational linguistics) usually includes speech analysis along with text analysis, but in this review speech analysis does not consider. The review used materials from original works, monographs, and a number of articles published the «Open Systems.DBMS» journal.

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Rohit Kumar Yadav, Aanchal Madaan, and Janu. "Comprehensive analysis of natural language processing." Global Journal of Engineering and Technology Advances 19, no. 1 (April 30, 2024): 083–90. http://dx.doi.org/10.30574/gjeta.2024.19.1.0058.

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Natural Language Processing (NLP) is a fascinating field of study that teaches computers to understand and use human language. This means that computers can read, write, and even translate text just like humans. NLP has many practical uses, such as categorizing text, identifying the tone of language, recognizing names in text, translating languages, and answering questions. NLP has come a long way since it was first developed. In the past, it relied on strict rules to understand language, but now it uses advanced techniques like machine learning and deep learning to understand text. However, there are still some challenges in NLP, such as understanding the meaning of words in context and considering cultural differences. Despite these challenges, NLP is being used in many different areas, from healthcare and finance to education and customer service. NLP is transforming the way humans interact with computers and is making it easier to extract important information from large amounts of text.

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14

Zhao, Liping, Waad Alhoshan, Alessio Ferrari, Keletso J. Letsholo, Muideen A. Ajagbe, Erol-Valeriu Chioasca, and Riza T. Batista-Navarro. "Natural Language Processing for Requirements Engineering." ACM Computing Surveys 54, no. 3 (June 2021): 1–41. http://dx.doi.org/10.1145/3444689.

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Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.

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15

Musthofa, Musthofa. "COMPUTATIONAL LINGUISTICS (Model Baru Kajian Linguistik dalam Perspektif Komputer)." Adabiyyāt: Jurnal Bahasa dan Sastra 9, no. 2 (December 31, 2010): 247. http://dx.doi.org/10.14421/ajbs.2010.09203.

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This paper describes a new discipline in applied linguistics studies, computational linguistics. It’s a new model of applied linguistics which is influenced by computer technology. Computational linguistics is a discipline straddling applied linguistics and computer science that is concerned with the computer processing of natural languages on all levels of linguistic description. Traditionally, computational linguistics was usually performed by computer scientists who had specialized in the application of computers to the processing of a natural language. Computational linguists often work as members of interdisciplinary teams, including linguists (specifically trained in linguistics), language experts (persons with some level of ability in the languages relevant to a given project), and computer scientists. The several areas of computational linguistics study encompasses such practical applications as speech recognition systems, speech synthesis, automated voice response systems, web search engines, text editors, grammar checking, text to speech, corpus linguistics, machine translation, text data mining, and others. This paper presents the definition of computational linguistics, relation between language and computer, and area of computational linguistics studies.

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Prema, Prof M., Prof Ramya M, and Prof V. Raju. "Natural Language Processing for Data Science Workforce Analysis." International Journal of Engineering and Advanced Technology 12, no. 2 (December 30, 2022): 114–18. http://dx.doi.org/10.35940/ijeat.b3947.1212222.

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As the demand for people with Data Science and Data Analysis skills are rising at a very high rate, periodic exploration of the skill sets for jobs in these fields have become essential. This research presents the use of Natural Language Processing for Human Resource Management. It presents the application of such techniques and tools as Python Libraries with Beautiful Soup and Selinimum, Web Scrapping, Topic Analysis, Sentiment Analysis, and Natural Language Processing.

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Ratianantitra, Volatiana Marielle. "A State of the Art Review on Natural Language Processing applied to the Malagasy Language." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 1–5. http://dx.doi.org/10.59200/icarti.2023.001.

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Based on the growing mass of information of all kinds to be processed and to facilitate human/machine dialogue, research teams and language industries are still developing applications integrating automatic natural language processing techniques. Natural Language Processing (NLP) is a discipline on the border of linguistics and computer science that concerns the application of computer programs and techniques to all aspects of human language. NLP is an area of research that is still open. It has extended its applications in various fields and applied them in several languages worldwide. This paper will review the NLP methods and resources that have been performed on the Malagasy language only. Recently, work on the automatic processing of the Malagasy language occurs even if the language is part of the group of under-endowed languages. This will allow researchers who are interested in the Malagasy language to update themselves in terms of the work already carried out, and the challenges that may be envisaged in future.

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18

Bajwa, Imran Sarwar. "Virtual Telemedicine Using Natural Language Processing." International Journal of Information Technology and Web Engineering 5, no. 1 (January 2010): 43–55. http://dx.doi.org/10.4018/jitwe.2010010103.

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Conventional telemedicine has limitations due to the existing time constraints in the response of a medical specialist. One major reason is that telemedicine based medical facilities are subject to the availability of a medical expert and telecommunication facilities. On the other hand, communication using telecommunication is only possible on fixed and appointed time. Typically, the field of telemedicine exists in both medical and telecommunication areas to provide medical facilities over a long distance, especially in remote areas. In this article, the authors present a solution for ‘virtual telemedicine’ to cope with the problem of the long time constraints in conventional telemedicine. Virtual Telemedicine is the use of telemedicine with the methods of artificial intelligence.

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19

Church, Kenneth W., and Lisa F. Rau. "Commercial applications of natural language processing." Communications of the ACM 38, no. 11 (November 1995): 71–79. http://dx.doi.org/10.1145/219717.219778.

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20

Lewis, David D., and Karen Spärck Jones. "Natural language processing for information retrieval." Communications of the ACM 39, no. 1 (January 1996): 92–101. http://dx.doi.org/10.1145/234173.234210.

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21

Dahl, Deborah A., Lewis M. Norton, and K. W. Scholz. "Commercialization of natural language processing technology." Communications of the ACM 43, no. 11es (November 2000): 7. http://dx.doi.org/10.1145/352515.352525.

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22

Stone, Allen. "Natural-Language Processing for Intrusion Detection." Computer 40, no. 12 (December 2007): 103–5. http://dx.doi.org/10.1109/mc.2007.437.

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23

Kim, Jin-Dong. "Biomedical Natural Language Processing." Computational Linguistics 43, no. 1 (April 2017): 265–67. http://dx.doi.org/10.1162/coli_r_00281.

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24

Yang, Junpu. "Research on Security Model Design Based on Computational Network and Natural Language Processing." Mobile Information Systems 2022 (August 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/7191312.

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Human logical thinking exists in the form of language, and most of the knowledge is also recorded and transmitted in the form of language. It is also an important and even core part of artificial intelligence. Communicating with computers in natural language is a long-standing pursuit of people. People can use the computer in the language they are most accustomed to and can also use it to learn more about human language abilities and intelligent mechanisms. The realization of natural language communication between humans and computers means that computers can not only understand the meaning of natural language texts but also express the intentions and thoughts given in natural language texts. This paper designs and studies a computational model for natural language processing (NLP) models for natural language processing. This paper aims to study the design of computing network security model based on natural language processing. This paper proposes three calculation models, which are based on the long-term and short-term memory neural network model (LSTM), FastText model, and text processing model (GCN) based on graph convolution neural network. Several natural language processing models are evaluated and analyzed using four indexes: accuracy, recall, exactness, and F1 vaule. Results show that the performance level of the GCN model is the best. The accuracy of the NLP recognition of this model reaches 86.66%, which is 2.93% and 1.55% higher than the accuracy of the LSTM model and the FastText model, respectively.

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Praful Bharadiya, Jasmin. "A Comprehensive Survey of Deep Learning Techniques Natural Language Processing." European Journal of Technology 7, no. 1 (May 23, 2023): 58–66. http://dx.doi.org/10.47672/ejt.1473.

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In NLP research, unsupervised or semi-supervised learning techniques are increasingly getting more attention. These learning techniques are capable of learning from data that has not been manually annotated with the necessary answers or by combining non-annotated and annotated data. This essay presents a survey of various natural language processing methods. The discipline of natural language processing, which integrates linguistics, artificial intelligence, and computer science, was established to make it easier for computers and human language to communicate with one another. It is, as we can say, relevant psychopathology for the study of computer-human interaction. The understanding of natural language, which entails enabling machines to naturally interpret human language, is one of the many challenges this area faces. Discourse analysis, morphological separation, machine translation, production and understanding of NLP, part-of-speech tagging, recognition of optical characters, speech recognition, and sentiment analysis are some of the most frequent NLP tasks. As opposed to learning, which is supervised and typically yields few correct results for a given amount of input data, this job is typically quite difficult. However, there is a sizable amount of data available that is unannotated in nature, i.e. the entire contents are available on the internet, and it typically yields less accurate findings.

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Araujo, Lourdes. "Genetic programming for natural language processing." Genetic Programming and Evolvable Machines 21, no. 1-2 (July 23, 2019): 11–32. http://dx.doi.org/10.1007/s10710-019-09361-5.

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Gupta, Rajiv. "Research Paper on Artificial Intelligence." International Journal of Engineering and Computer Science 12, no. 02 (February 18, 2023): 25654–0656. http://dx.doi.org/10.18535/ijecs/v12i02.4720.

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This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. Artificial intelligence is working a lot in decreasing human effort but with less growth.

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Sulistyo, Danang, Fadhli Ahda, and Vivi Aida Fitria. "Epistomologi dalam Natural Language Processing." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 9 (September 26, 2021): 652–64. http://dx.doi.org/10.17977/um068v1i92021p652-664.

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How to obtain the truth about knowledge by considering the axiology and anthology aspects of knowledge is the challenge that epistemology must solve. While in scientific epistemology, the accumulation of information that is true will affect how inquiries about the universe are answered heuristically and how natural occurrences are predicted. The primary goal and aim of epistemology, a subfield of philosophy of science, is to investigate and ascertain the nature of knowledge. As such, it examines the origin, sources, and importance of validity from knowledge in addition to discussing the extent and veracity of science. The goal of NLP, a branch of artificial intelligence (AI), is to enable computers to comprehend human language. For instance, text and voice, which people frequently utilize in casual discussions. Integrating computational linguistics with predictive methods led to the development of NLP. NLP has so far done well with text and audio data. There are still others who believe that NLP is in decline, particularly when it comes to managing idioms and sarcasm in contextual data. Due to the vast number of local languages spoken worldwide, the millions of words they contain, the hundreds of regional accents, and their importance in preventing the extinction of local languages, even machine translation, which was the initial purpose of NLP, may still be investigated further. Masalah yang harus dihadapi oleh Epistomologi adalah bagaimana mendapatkan kebenaran akan pengetahuan dengan menimbang aspek antologi dan aksiologi pada pengetahuan. Sedangkan pada epistomologi ilmiah, penyusunan kebenaran suatu pengetahuan akan berpengaruh untuk menjawab pertanyaan di dunia secara heuristis serta dalam memprediksi fenomena alam yang terjadi. Mempelajari dan menentukan hakikat dari suatu pengetahuan adalah fungsi dan tugas utama epistomologi sebagai salah satu cabang dari filsafat ilmu, maka tidak hanya berbicara tentang kebenaran ilmu pengetahuan dan ruang lingkup pengetahuan, akan tetapi secara luas epistomologi juga mempelajari tentang asal mula, sumber dan juga nilai validitas dari pengetahuan. Pemrosesan bahasa alami, atau NLP, adalah bagian dari kecerdasan buatan (AI) yang berkaitan dengan memberi komputer kemampuan untuk memahami bahasa alami manusia. Misalnya teks dan suara yang sering digunakan manusia dalam percakapan sehari-hari. NLP dibuat dengan menggabungkan linguistik komputasi dengan model statistic. Sampai saat ini NLP memiliki performa yang baik pada data teks dan audio. Namun, masih ada orang yang menilai penurunan dunia NLP, terutama dalam penanganan sarkasme dan idiom dalam data kontekstual. Bahkan terjemahan mesin yang merupakan tujuan awal NLP masih dapat dieksplorasi lebih dalam, karena ada banyak bahasa lokal di dunia, ada jutaan kata, ratusan aksen lokal, dan perannya untuk menyelamatkan Bahasa Lokal dari kepunahan.

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Zhang, Xiaofeng. "Chart Symbol Recognition Based on Computer Natural Language Processing." Journal of Coastal Research 83, sp1 (May 4, 2019): 724. http://dx.doi.org/10.2112/si83-120.1.

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GAIZAUSKAS, R., P. J. RODGERS, and K. HUMPHREYS. "Visual Tools for Natural Language Processing." Journal of Visual Languages & Computing 12, no. 4 (August 2001): 375–412. http://dx.doi.org/10.1006/jvlc.2000.0203.

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Bailin, Alan, and Philip Thomson. "The use of natural language processing in computer-assisted language instruction." Computers and the Humanities 22, no. 2 (June 1988): 99–110. http://dx.doi.org/10.1007/bf00057649.

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Goto, Isao. "Python for Natural Language Processing." Journal of The Institute of Image Information and Television Engineers 72, no. 11 (2018): 909–12. http://dx.doi.org/10.3169/itej.72.909.

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Han, Xu, Zhengyan Zhang, and Zhiyuan Liu. "Knowledgeable machine learning for natural language processing." Communications of the ACM 64, no. 11 (November 2021): 50–51. http://dx.doi.org/10.1145/3481608.

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Li, Yong, Xiaojun Yang, Min Zuo, Qingyu Jin, Haisheng Li, and Qian Cao. "Deep Structured Learning for Natural Language Processing." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 3 (July 9, 2021): 1–14. http://dx.doi.org/10.1145/3433538.

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The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.

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Manaris, B. Z., and B. M. Slator. "Interactive Natural Language Processing: Building on Success." Computer 29, no. 7 (July 1996): 28. http://dx.doi.org/10.1109/mc.1996.511965.

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Taskin, Zehra, and Umut Al. "Natural language processing applications in library and information science." Online Information Review 43, no. 4 (August 12, 2019): 676–90. http://dx.doi.org/10.1108/oir-07-2018-0217.

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Purpose With the recent developments in information technologies, natural language processing (NLP) practices have made tasks in many areas easier and more practical. Nowadays, especially when big data are used in most research, NLP provides fast and easy methods for processing these data. The purpose of this paper is to identify subfields of library and information science (LIS) where NLP can be used and to provide a guide based on bibliometrics and social network analyses for researchers who intend to study this subject. Design/methodology/approach Within the scope of this study, 6,607 publications, including NLP methods published in the field of LIS, are examined and visualized by social network analysis methods. Findings After evaluating the obtained results, the subject categories of publications, frequently used keywords in these publications and the relationships between these words are revealed. Finally, the core journals and articles are classified thematically for researchers working in the field of LIS and planning to apply NLP in their research. Originality/value The results of this paper draw a general framework for LIS field and guides researchers on new techniques that may be useful in the field.

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Dale, Robert, Hermann Moisl, and Harold Somers. "Handbook of Natural Language Processing." Computational Linguistics 27, no. 4 (December 2001): 602–3. http://dx.doi.org/10.1162/coli.2000.27.4.602.

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Nagata, Noriko. "An Effective Application of Natural Language Processing in Second Language Instruction." CALICO Journal 13, no. 1 (January 14, 2013): 47–67. http://dx.doi.org/10.1558/cj.v13i1.47-67.

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This paper presents an intelligent CALI system called "Nihongo-CALI" (Japanese Computer Assisted Language Instruction), which employs natural language processing to provide immediate, grammatically sophisticated feedback to students in an interactive environment. Using this system, Nagata (1993) previously compared the effectiveness of the two different levels of computer feedback for teaching Japanese passive sentences: traditional feedback (which follows simple pattern-matching error analysis and indicates only missing/unexpected words in the learners' responses) and intelligent feedback (which utilizes a parsing technique to provide detailed grammatical explanations for the source of the learners' errors). The study found a statistically significant difference between traditional and intelligent feedback, favoring intelligent feedback. The present study compares the efficacy of intelligent CALI feedback with that of a more advanced, traditional CALI feedback (which also indicates the positions of missing particles in the learners' responses) for teaching basic sentence constructions in Japanese. The result indicates that the Intelligent CALI feedback is more effective than even the enhanced version of traditional CALI feedback, underscoring the importance of natural language processing technology in second language instruction.

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Zheng, Wenfeng, Mingzhe Liu, Kenan Li, and Xuan Liu. "AI for Computational Vision, Natural Language Processing, and Geoinformatics." Applied Sciences 13, no. 24 (December 15, 2023): 13276. http://dx.doi.org/10.3390/app132413276.

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Qiao, Jianfeng, Xingzhi Yan, and Shuran Lv. "Natural Language Processing Using Neighbour Entropy-based Segmentation." Journal of Computing and Information Technology 29, no. 2 (July 4, 2022): 113–31. http://dx.doi.org/10.20532/cit.2021.1005393.

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In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018.

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Kuzminov, I. F., P. D. Bakhtin, A. A. Timofeev, E. E. Khabirova, P. A. Lobanova, and N. I. Zurabyan. "Modern Natural Language Processing Technologies for Strategic Analytics." Scientific and Technical Information Processing 48, no. 6 (December 2021): 467–75. http://dx.doi.org/10.3103/s0147688221060071.

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Guthrie, Louise, James Pustejovsky, Yorick Wilks, and Brian M. Slator. "The role of lexicons in natural language processing." Communications of the ACM 39, no. 1 (January 1996): 63–72. http://dx.doi.org/10.1145/234173.234204.

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Kreutzer, T., P. Vinck, P. N. Pham, A. An, L. Appel, E. DeLuca, G. Tang, et al. "Improving humanitarian needs assessments through natural language processing." IBM Journal of Research and Development 64, no. 1/2 (January 1, 2020): 9:1–9:14. http://dx.doi.org/10.1147/jrd.2019.2947014.

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Al-Khalifa, Hend S., Taif AlOmar, and Ghala AlOlyyan. "Natural Language Processing Patents Landscape Analysis." Data 9, no. 4 (March 31, 2024): 52. http://dx.doi.org/10.3390/data9040052.

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Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain.

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Bird, Steven. "Natural Language Processing and Linguistic Fieldwork." Computational Linguistics 35, no. 3 (September 2009): 469–74. http://dx.doi.org/10.1162/coli.35.3.469.

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Louis, Annie. "Natural Language Processing for Social Media." Computational Linguistics 42, no. 4 (December 2016): 833–36. http://dx.doi.org/10.1162/coli_r_00270.

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Duh, Kevin. "Bayesian Analysis in Natural Language Processing." Computational Linguistics 44, no. 1 (March 2018): 187–89. http://dx.doi.org/10.1162/coli_r_00310.

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Dou, Zi-Yi, Xing Wang, Shuming Shi, and Zhaopeng Tu. "Exploiting deep representations for natural language processing." Neurocomputing 386 (April 2020): 1–7. http://dx.doi.org/10.1016/j.neucom.2019.12.060.

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da Rocha, Ricardo Luis de Azevedo. "Adaptive Technology Applied to Natural Language Processing." IEEE Latin America Transactions 5, no. 7 (November 2007): 544–51. http://dx.doi.org/10.1109/t-la.2007.4445755.

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N., Deepika, and Guruprasad N. "SPEECH/TEXT TO INDIAN SIGN LANGUAGE USING NATURAL LANGUAGE PROCESSING." Indian Journal of Computer Science and Engineering 14, no. 3 (June 20, 2023): 551–60. http://dx.doi.org/10.21817/indjcse/2023/v14i3/231403030.

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