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Journal articles on the topic 'Natural Language Processing'

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Published: 4 June 2021
Last updated: 7 September 2024

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1

Patel, Stuti. "Natural Language Processing." International Journal of Science and Research (IJSR) 12, no. 2 (February 5, 2023): 1123–29. http://dx.doi.org/10.21275/sr23216193027.

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2

Lone, Nawaz Ali, Kaiser J. Giri, and Rumaan Bashir. "Natural Language Processing Resources for the Kashmiri Language." Indian Journal Of Science And Technology 15, no. 43 (November 20, 2022): 2275–81. http://dx.doi.org/10.17485/ijst/v15i43.1964.

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3

Vilson, Minu Mariya, Menon Bhavana Rajan, and Mrs Ann Rija Paul. "Motion of curtains using Natural Language Processing." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1840–43. http://dx.doi.org/10.31142/ijtsrd23199.

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4

Niimi, Akio. "Natural Language Processing." Chest 159, no. 6 (June 2021): 2149–50. http://dx.doi.org/10.1016/j.chest.2021.01.045.

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5

HASHIDA, KOICHI. "Natural Language Processing." Journal of the Institute of Electrical Engineers of Japan 121, no. 3 (2001): 195–98. http://dx.doi.org/10.1541/ieejjournal.121.195.

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6

N. O. Sadiku, Matthew, Yu Zhou, and Sarhan M. Musa. "Natural Language Processing." International Journal of Advances in Scientific Research and Engineering 4, no. 5 (2018): 68–70. http://dx.doi.org/10.31695/ijasre.2018.32708.

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7

Covington, Michael A., Fernando C. N. Pereira, and Barbara J. Grosz. "Natural Language Processing." Language 71, no. 3 (September 1995): 652. http://dx.doi.org/10.2307/416262.

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8

Jain, Aditya, Gandhar Kulkarni, and Vraj Shah. "Natural Language Processing." International Journal of Computer Sciences and Engineering 6, no. 1 (January 31, 2018): 161–67. http://dx.doi.org/10.26438/ijcse/v6i1.161167.

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9

Yilmaz, A. Egemen. "Natural Language Processing." International Journal of Systems and Service-Oriented Engineering 4, no. 1 (January 2014): 68–83. http://dx.doi.org/10.4018/ijssoe.2014010105.

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Requirement analysis is the very first and crucial step in the software development processes. On the other hand, as previously addressed by other researchers, it is the Achilles' heel of the whole process since the requirements lie on the problem space, whereas other software artifacts are on the solution space. Stating the requirements in a clear manner eases the following steps in the process as well as reducing the number of potential errors. In this paper, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the requirement quality attributes via lexical and syntactic analysis methods sometimes with generic, and sometimes domain and application specific knowledge bases.
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10

Weischedel, R., J. Carbonell, B. Grosz, W. Lehnert, M. Marcus, R. Perrault, and R. Wilensky. "Natural Language Processing." Annual Review of Computer Science 4, no. 1 (June 1990): 435–52. http://dx.doi.org/10.1146/annurev.cs.04.060190.002251.

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11

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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12

Patten, T., and P. Jacobs. "Natural-language processing." IEEE Expert 9, no. 1 (February 1994): 35. http://dx.doi.org/10.1109/64.295134.

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13

Rindflesch, Thomas C. "Natural Language Processing." Annual Review of Applied Linguistics 16 (March 1996): 70–85. http://dx.doi.org/10.1017/s0267190500001446.

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Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.
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14

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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15

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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16

JOSHI, A. K. "Natural Language Processing." Science 253, no. 5025 (September 13, 1991): 1242–49. http://dx.doi.org/10.1126/science.253.5025.1242.

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17

Grosz, Barbara J. "Natural-language processing." Artificial Intelligence 25, no. 1 (January 1985): 1–4. http://dx.doi.org/10.1016/0004-3702(85)90038-4.

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18

Horacek, Helmut. "Natural language processing." Computer Physics Communications 61, no. 1-2 (November 1990): 76–92. http://dx.doi.org/10.1016/0010-4655(90)90107-c.

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19

Chowdhury, Gobinda G. "Natural language processing." Annual Review of Information Science and Technology 37, no. 1 (January 31, 2005): 51–89. http://dx.doi.org/10.1002/aris.1440370103.

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20

Hirschberg, Julia, Bruce W. Ballard, and Donald Hindle. "Natural Language Processing." AT&T Technical Journal 67, no. 1 (January 2, 1988): 41–57. http://dx.doi.org/10.1002/j.1538-7305.1988.tb00232.x.

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21

Chandrasekaran, Arvind. "Natural Language Processing." International Journal on Cybernetics & Informatics 12, no. 2 (March 11, 2023): 57–61. http://dx.doi.org/10.5121/ijci.2023.120205.

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Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine reports from the healthcare facilities, secondary data like attendance monitoring at workplaces and schools, the web, and the media play a significant informational source with more than 60% of the initial outbreak reporting to the informal sources. Through the application of natural language processing methods and machine learning technologies, a pipeline is developed which extracts the critical entities like country, confirmed case counts, disease, and case dates, which are mandatory entities from the epidemiological article and are saved in the database thereby facilitating the data entry easier. The advantages are the facilitation of relevant score articles shown first, thereby providing the web service results termed EventEpi integrated into the Event Based Surveillance (EBS) workflows.
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22

Németh, Renáta, and Júlia Koltai. "Natural language processing." Intersections 9, no. 1 (April 26, 2023): 5–22. http://dx.doi.org/10.17356/ieejsp.v9i1.871.

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Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outside of sociology, while the expertise of sociology has been based on its own methods of research. Another challenge is epistemological: it is related to the validity of digital data and the different viewpoints associated with predictive and causal approaches. In our paper, we discuss the challenges and opportunities of the use of NLP in sociology, offer some potential solutions to the concerns and provide meaningful and diverse examples of its sociological application, most of which are related to research on Eastern European societies. The focus will be on the use of NLP in quantitative text analysis. Solutions are provided concerning how sociological knowledge can be incorporated into the new methods and how the new analytical tools can be evaluated against the principles of traditional quantitative methodology.
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23

Martinez, Angel R. "Natural language processing." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 3 (March 1, 2010): 352–57. http://dx.doi.org/10.1002/wics.76.

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24

Marcus, M. "New trends in natural language processing: statistical natural language processing." Proceedings of the National Academy of Sciences 92, no. 22 (October 24, 1995): 10052–59. http://dx.doi.org/10.1073/pnas.92.22.10052.

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25

Chikkarangaiah, Jayanth, Adarsh Uday, David De Hilster, Shobha Gangadhar, and Jyoti Shetty. "Enhancing the English natural language processing dictionary using natural language processing++." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (September 1, 2024): 3466. http://dx.doi.org/10.11591/ijai.v13.i3.pp3466-3477.

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<p><span lang="EN-US">Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enhancement of the English dictionary using a more versatile and robust programming language known as NLP++, a plugin to distributed big data analytics platforms such as HPCC systems. The unique features of NLP++ language is the enabler for realization of the proposed approach. This paper also discusses key NLP techniques, dictionary refinements analysis using NLP and NLP++. The results show that the proposed approach using NLP++ has significantly improved the accuracy and comprehensiveness of the English dictionary.</span></p>
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26

Sawant, Isha Sudhir. "Natural Language Processing (NLP) from Image to Text." International Journal of Research Publication and Reviews 5, no. 5 (May 26, 2024): 13103–9. http://dx.doi.org/10.55248/gengpi.5.0524.1463.

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27

Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.

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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.
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28

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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29

Salt, Jessica, Polina Harik, and Michael A. Barone. "Leveraging Natural Language Processing." Academic Medicine 94, no. 3 (March 2019): 314–16. http://dx.doi.org/10.1097/acm.0000000000002558.

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30

Farghaly, Ali, and Khaled Shaalan. "Arabic Natural Language Processing." ACM Transactions on Asian Language Information Processing 8, no. 4 (December 2009): 1–22. http://dx.doi.org/10.1145/1644879.1644881.

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31

Grefenstette, Gregory, and Frédérique Segond. "Multilingual Natural Language Processing." International Journal of Corpus Linguistics 2, no. 1 (January 1, 1997): 153–62. http://dx.doi.org/10.1075/ijcl.2.1.08gre.

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32

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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33

Raval, Abhilasha, and Kashyp Dave. "Natural Language Processing- AI." INTERNATIONAL JOURNAL OF ADVANCE RESEARCH IN COMPUTER SCIENCE AND MANAGEMENT STUDIES 11, no. 7 (July 2023): 20–23. http://dx.doi.org/10.61161/ijarcsms.v11i7.5.

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34

G, Dr Chandrika. "Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (June 30, 2024): 1092–95. http://dx.doi.org/10.22214/ijraset.2024.63281.

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Abstract: Machines utilize Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), to comprehend, evaluate, and translate human languages. Due to advancements in information and communication technology over the last ten years, NLP has gained increased awareness and has been the subject of several studies. Understanding the steps required to advance literary understanding is so crucial. The study's objective is to provide an organized review of the literature in NLP research using bibliometric analysis. In the discipline of NLP, the study highlights important research fields, research clusters, cited publications, significant authors, institutions, nations, and publishing patterns. From the Web of Science (WoS) database, 12541 NLP papers were taken out and subjected to further bibliometric analysis The outcome showed that the largest number of NLP publications occurred in 2021, with the first publication being in 1989. The leading journal with the most publications was IEEE Access, and NLP papers have earned the most citations—3174—than any other journal. Liu HF is the most productive author in the field of NLP, and Harward University is the most significant organization. When it comes to the overall amount of publications, the US is the top nation. Researchers looked at the applied sciences in great detail.
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35

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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36

G, Sajini. "Computational Evaluation of Language Models by Considering Various Scaling Properties for Processing Natural Languages." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 691–700. http://dx.doi.org/10.5373/jardcs/v12sp7/20202159.

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37

Dudaš, Adam, and Jarmila Skrinarova. "Natural Language Processing in Translation of Relational Languages." IPSI Transactions on Internet Research 19, no. 01 (January 1, 2023): 17–23. http://dx.doi.org/10.58245/ipsi.tir.2301.04.

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Methods of data manipulation in combination with sturcture of the data and integrity constraints define data model used in the relational databases. This article focuses on the methods and processes of operation sets which are used for selection of data from relational database and translation between various formats of this manipulation. The article presents the design, implementation and experimental evaluation of tool for translating between relational algebra, tuple relational calculus, Structured Query Language and unrestricted natural language in all directions. Presented software tool translates from natural language with the use of artificial intelligence methods for natural language processing, syntactic analysis and parsing. Generative pretrained transformer of third generation (GPT3) based Codex – Davinci translation model was used as an artificial intelligence method used in the workflow of translation from English language to relational languages.
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38

Minhwa Chung and D. Moldevan. "Applying parallel processing to natural-language processing." IEEE Expert 9, no. 1 (February 1994): 36–44. http://dx.doi.org/10.1109/64.295133.

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39

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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40

Geetha, Dr V., Dr C. K. Gomathy, Mr P. V. Sri Ram, and Surya Prakash L N. "NOVEL STUDY ON 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/ijsrem27091.

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Natural Language Processing (NLP) is the tech wiz working tirelessly to break down language barriers between us and our devices. It's the reason our smart phone, tablet or laptop understands our voice commands and translates our languages in a second. NLP is like giving machines the ability to comprehend and respond to language nuances, turning our interactions into seamless conversations. Think of it as the digital polyglot that not only reads but truly understands the messages we convey, from the simplest text to the most intricate emotions. From predictive text to chatbots, NLP is the digital linguist enhancing the way we communicate with our devices, making technology feel more like a conversation with a helpful virtual friend. Keywords: seamless conversations, digital polyglot, predictive text, chatbots, digital linguist, technology communication, virtual friend.
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41

IKEDA, Takashi, and Tadahiro MATSUMOTO. "Braille, Sign Language and Natural Language Processing." IEICE ESS FUNDAMENTALS REVIEW 4, no. 4 (2011): 282–92. http://dx.doi.org/10.1587/essfr.4.282.

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42

Ward, R. D. "Natural Language Processing and the Language‐Impaired." PLET: Programmed Learning & Educational Technology 23, no. 2 (May 1986): 144–49. http://dx.doi.org/10.1080/0033039860230208.

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43

Sharma, Sapna. "Natural Language Processing in AI: Language Interpreter." Journal of Artificial Intelligence Research & Advances 7, no. 2 (August 1, 2020): 21–27. http://dx.doi.org/10.37591/joaira.v7i2.2538.

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44

Sadiku, Matthew N. O., Yu Zhou, and Sarhan M. Musa. "NATURAL LANGUAGE PROCESSING IN HEALTHCARE." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 39. http://dx.doi.org/10.23956/ijarcsse.v8i5.626.

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Natural language processing (NLP) refers to the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written communication. Healthcare is the biggest user of the NLP tools. It is expected that NLP tools should be able to bridge the gap between the mountain of data generated daily and the limited cognitive capacity of the human mind. This paper provides a brief introduction on the use of NLP in healthcare.
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45

Radev, Dragomir R., and Rada Mihalcea. "Networks and Natural Language Processing." AI Magazine 29, no. 3 (September 5, 2008): 16. http://dx.doi.org/10.1609/aimag.v29i3.2160.

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Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.
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46

Van Vleck, Tielman T., Douglas Farrell, and Lili Chan. "Natural Language Processing in Nephrology." Advances in Chronic Kidney Disease 29, no. 5 (September 2022): 465–71. http://dx.doi.org/10.1053/j.ackd.2022.07.001.

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47

Mochihashi, Daichi. "Natural Language Processing in Robotics." Journal of the Robotics Society of Japan 39, no. 5 (2021): 399–404. http://dx.doi.org/10.7210/jrsj.39.399.

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48

Alharbi, Mohammad, Matthew Roach, Tom Cheesman, and Robert S. Laramee. "VNLP: Visible natural language processing." Information Visualization 20, no. 4 (August 13, 2021): 245–62. http://dx.doi.org/10.1177/14738716211038898.

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In general, Natural Language Processing (NLP) algorithms exhibit black-box behavior. Users input text and output are provided with no explanation of how the results are obtained. In order to increase understanding and trust, users value transparent processing which may explain derived results and enable understanding of the underlying routines. Many approaches take an opaque approach by default when designing NLP tools and do not incorporate a means to steer and manipulate the intermediate NLP steps. We present an interactive, customizable, visual framework that enables users to observe and participate in the NLP pipeline processes, explicitly manipulate the parameters of each step, and explore the result visually based on user preferences. The visible NLP (VNLP) pipeline design is then applied to a text similarity application to demonstrate the utility and advantages of a visible and transparent NLP pipeline in supporting users to understand and justify both the process and results. We also report feedback on our framework from a modern languages expert.
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49

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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50

Covington, Michael A., Madeleine Bates, and Ralph M. Weischedel. "Challenges in Natural Language Processing." Language 71, no. 2 (June 1995): 402. http://dx.doi.org/10.2307/416182.

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