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Journal articles on the topic 'Natural language processing techniques'

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Published: 10 December 2022

Last updated: 29 January 2023

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1

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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Valiyev, Giavid, Marcello Piraino, Arvid Kok, Michael Street, Ivana Ilic Mestric, and Retzius Birger. "Initial Exploitation of Natural Language Processing Techniques on NATO Strategy and Policies." Information & Security: An International Journal 47, no. 2 (2020): 187–202. http://dx.doi.org/10.11610/isij.4713.

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Pujeri, Bhagyashree P., and Jagadeesh Sai D. "An Anatomization of Language Detection and Translation using NLP Techniques." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (December 10, 2020): 69–77. http://dx.doi.org/10.35940/ijitee.b8265.1210220.

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The issue with identifying language relates to process of determining natural language in which specific text is written. This is one of the big difficulties in the processing of natural languages. Still, they also pose a problem in improving multiclass classification in this area. Language detection and translation a significant Language Identification task are required. The language analysis method may be carried out according to tools available in a particular language if the source language is known. A successful language detection algorithm determines the achievement of the sentiment analysis task and other identification tasks. Processing natural language and machine learning techniques involve knowledge that is annotated with its language. Algorithms for natural language processing must be updated according to language's grammar.This paper proposes a secure language detection and translation technique to solve the security in natural language processing problems. Language detection algorithm based on char n-gram based statistical detector and translation Yandex API is used.While translating, there should be encryption and decryption for that we are using AES Algorithm.

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H.Yousif, Jabar. "Natural Language Processing based Soft Computing Techniques." International Journal of Computer Applications 77, no. 8 (September 18, 2013): 43–49. http://dx.doi.org/10.5120/13418-1089.

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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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Korycinski, C., and Alan F. Newell. "Natural-language processing and automatic indexing." Indexer: The International Journal of Indexing: Volume 17, Issue 1 17, no. 1 (April 1, 1990): 21–29. http://dx.doi.org/10.3828/indexer.1990.17.1.8.

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The task of producing satisfactory indexes by automatic means has been tackled on two fronts: by statistical analysis of text and by attempting content analysis of the text in much the same way as a human indexcr does. Though statistical techniques have a lot to offer for free-text database systems, neither method has had much success with back-of-the-bopk indexing. This review examines some problems associated with the application of natural-language processing techniques to book texts.

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Panchal, Drashti, Mihika Mehta, Aryaman Mishra, Saish Ghole, and Mrs Smita Dandge. "Sentiment Analysis Using Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2262–66. http://dx.doi.org/10.22214/ijraset.2022.42711.

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Abstract: In recent years, there has been an increasing interest in using natural language processing (NLP) to perform sentiment analysis. This is because NLP can help to automatically extract and identify the sentiment expressed in text data, which is often more accurate and reliable than using human annotation. There are a variety of NLP techniques that can be used for sentiment analysis, including opinion mining, text classification, and lexical analysis. Each of these methods has its own advantages and disadvantages, and the choice of technique will often depend on the type and quality of the text data that is available. In general, sentiment analysis using NLP is a very promising area of research with many potential applications. As more and more text data is generated, it will become increasingly important to be able to automatically extract the sentiment expressed in this data.

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Jain, Dr Meghna. "Sentiment Classification of Hindi Language using Natural Language Processing Techniques." Journal of Language and Linguistics in Society, no. 26 (November 21, 2022): 7–10. http://dx.doi.org/10.55529/jlls.26.7.10.

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This paper has presented Hybrid Approach for determination of sentimental phrase or words from Hindi text automatically through use of Hindi sentiment’s lexicon and classifying them into polarity i.e. Positive, Negative and Neutral.

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Fairie, Paul, Zilong Zhang, Adam G. D'Souza, Tara Walsh, Hude Quan, and Maria J. Santana. "Categorising patient concerns using natural language processing techniques." BMJ Health & Care Informatics 28, no. 1 (June 2021): e100274. http://dx.doi.org/10.1136/bmjhci-2020-100274.

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ObjectivesPatient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.MethodsPatient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.ResultsThe LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).DiscussionLDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.ConclusionOur findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

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Nohria, Ankita, and Harkiran Kaur. "Evaluation of Parsing Techniques in Natural Language Processing." International Journal of Computer Trends and Technology 60, no. 1 (June 25, 2018): 31–34. http://dx.doi.org/10.14445/22312803/ijctt-v60p104.

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Zeng, Zhiqiang, Hua Shi, Yun Wu, and Zhiling Hong. "Survey of Natural Language Processing Techniques in Bioinformatics." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/674296.

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Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

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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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Rasheed, Fahad, Mehmoon Anwar, and Imran Khan. "Detecting Cyberbullying in Roman Urdu Language Using Natural Language Processing Techniques." Pakistan Journal of Engineering and Technology 5, no. 2 (September 19, 2022): 198–203. http://dx.doi.org/10.51846/vol5iss2pp198-203.

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Nowadays, social media platforms are the primary source of public communication and information. Social media platforms have become an integral part of our daily lives, and their user base is rapidly expanding as access is extended to more remote locations. Pakistan has around 71.70 million social media users that utilize Roman Urdu to communicate. With these improvements and the increasing number of users, there has been an increase in digital bullying, often known as cyberbullying. This research focuses on social media users who use Roman Urdu (Urdu language written in the English alphabet) to communicate. In this research, we explored the topic of cyberbullying actions on the Twitter platform, where users employ Roman Urdu as a medium of communication. To our knowledge, this is one of the very few studies that address cyberbullying behavior in Roman Urdu. Our proposed study aims to identify a suitable model for classifying cyberbullying behavior in Roman Urdu. To begin, the dataset was designed by extracting data from twitter using twitter's API. The targeted data was extracted using keywords based on Roman Urdu. The data was then annotated as bully and not-bully. After that, the dataset has been pre-processed to reduce noise, which includes punctuation, stop words, null entries, and duplication removal. Following that, features are extracted using two different methods, Count-Vectorizer and TF-IDF Vectorizer, and a set of ten different learning algorithms including SVM, MLP, and KNN was applied to both types of extracted features based on supervised learning. Support Vector Machine (SVM) performed the best out of the implemented algorithms by both combinations, with 97.8 percent when implemented over the TF-IDF features and 93.4 percent when implemented over the CV features. The proposed mechanism could be helpful for online social apps and chat rooms for the better detection and designing of bully word filters, making safer cyberspace for end users.

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Kunekar, Pankaj, Haripriya Arya, Abhishek Dighekar, Atharv Bagade, Harish Garud, and Eissa Abdelbari. "Restaurant Review Analysis using Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 1064–67. http://dx.doi.org/10.22214/ijraset.2022.48109.

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Abstract: The most effective tool any restaurant can have is the capability to track the daily sales of their food and beverage. Currently, recommendation systems plays an important role in both academia and industry. These are very helpful to manage an overload of information. In this paper, applied machine learning techniques for user reviews were used and valuable information in the reviews were analyzed. For both the customers and the owners, reviews are useful to make data-driven decisions. We built a machine learning model with Natural Language Processing techniques which captures a user’s opinions from user’s reviews. A lot of businesses fail due to the lack of profit and a lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to enhance their productivity.

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Mazzei, Daniele, Filippo Chiarello, and Gualtiero Fantoni. "Analyzing Social Robotics Research with Natural Language Processing Techniques." Cognitive Computation 13, no. 2 (January 16, 2021): 308–21. http://dx.doi.org/10.1007/s12559-020-09799-1.

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Araujo, L. "Symbiosis of Evolutionary Techniques and Statistical Natural Language Processing." IEEE Transactions on Evolutionary Computation 8, no. 1 (February 2004): 14–27. http://dx.doi.org/10.1109/tevc.2003.818189.

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Madeja, Matej, and Jaroslav Porubän. "Unit Under Test Identification Using Natural Language Processing Techniques." Open Computer Science 11, no. 1 (December 17, 2020): 22–32. http://dx.doi.org/10.1515/comp-2020-0150.

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AbstractUnit under test identification (UUT) is often difficult due to test smells, such as testing multiple UUTs in one test. Because the tests best reflect the current product specification they can be used to comprehend parts of the production code and the relationships between them. Because there is a similar vocabulary between the test and UUT, five NLP techniques were used on the source code of 5 popular Github projects in this paper. The collected results were compared with the manually identified UUTs. The tf-idf model achieved the best accuracy of 22% for a right UUT and 57% with a tolerance up to fifth place of manual identification. These results were obtained after preprocessing input documents with java keywords removal and word split. The tf-idf model achieved the best model training time and the index search takes within 1s per request, so it could be used in an Integrated Development Environment (IDE) as a support tool in the future. At the same time, it has been found that, for document preprocessing, word splitting improves accuracy best and removing java keywords has just a small improvement for tf-idf model results. Removing comments only slightly worsens the accuracy of Natural Language Processing (NLP) models. The best speed provided the word splitting with average 0.3s preprocessing time per all documents in a project.

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Métais, Elisabeth. "Enhancing information systems management with natural language processing techniques." Data & Knowledge Engineering 41, no. 2-3 (June 2002): 247–72. http://dx.doi.org/10.1016/s0169-023x(02)00043-5.

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Orooji, Azam, and Mostafa Langarizadeh. "Using of Natural Language Processing Techniques in Suicide Research." Emerging Science Journal 1, no. 2 (September 19, 2017): 89. http://dx.doi.org/10.28991/esj-2017-01120.

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It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

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Sangers, Jordy, Flavius Frasincar, Frederik Hogenboom, and Vadim Chepegin. "Semantic Web service discovery using natural language processing techniques." Expert Systems with Applications 40, no. 11 (September 2013): 4660–71. http://dx.doi.org/10.1016/j.eswa.2013.02.011.

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Kanaparthi, Vijaya. "Examining Natural Language Processing Techniques in the Education and Healthcare Fields." International Journal of Engineering and Advanced Technology 12, no. 2 (December 30, 2022): 8–18. http://dx.doi.org/10.35940/ijeat.b3861.1212222.

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Natural language processing is a branch of artificial intelligence currently being used to classify unstructured data. While natural language processing is found throughout several fields, these algorithms are currently being excelled in the education and healthcare fields. The healthcare industry has found various uses of natural language processing models. These algorithms are capable of analyzing large amounts of unstructured data from clinical notes, making it easier for healthcare professionals to identify at-risk patients and analyze consumer healthcare perception. In the education field, researchers are utilizing natural language processing models to enhance student academic success, reading comprehension, and to evaluate the fairness of student evaluations. Both fields have been able to find use of natural language model processing models. Some business leaders, however, are fearful of natural language processing. This review seeks to explore the various uses of natural language processing in the healthcare and education fields to determine the benefit and disadvantages these models have on both fields.

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Montejo-Ráez, Arturo, and Salud María Jiménez-Zafra. "Current Approaches and Applications in Natural Language Processing." Applied Sciences 12, no. 10 (May 11, 2022): 4859. http://dx.doi.org/10.3390/app12104859.

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Renjit, Sara, and Sumam Idicula. "Natural language inference for Malayalam language using language agnostic sentence representation." PeerJ Computer Science 7 (May 4, 2021): e508. http://dx.doi.org/10.7717/peerj-cs.508.

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Natural language inference (NLI) is an essential subtask in many natural language processing applications. It is a directional relationship from premise to hypothesis. A pair of texts is defined as entailed if a text infers its meaning from the other text. The NLI is also known as textual entailment recognition, and it recognizes entailed and contradictory sentences in various NLP systems like Question Answering, Summarization and Information retrieval systems. This paper describes the NLI problem attempted for a low resource Indian language Malayalam, the regional language of Kerala. More than 30 million people speak this language. The paper is about the Malayalam NLI dataset, named MaNLI dataset, and its application of NLI in Malayalam language using different models, namely Doc2Vec (paragraph vector), fastText, BERT (Bidirectional Encoder Representation from Transformers), and LASER (Language Agnostic Sentence Representation). Our work attempts NLI in two ways, as binary classification and as multiclass classification. For both the classifications, LASER outperformed the other techniques. For multiclass classification, NLI using LASER based sentence embedding technique outperformed the other techniques by a significant margin of 12% accuracy. There was also an accuracy improvement of 9% for LASER based NLI system for binary classification over the other techniques.

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Muthee, Mutwiri George, Mutua Makau, and Omamo Amos. "review of techniques for morphological analysis in natural language processing." African Journal of Science, Technology and Social Sciences 1, no. 2 (December 23, 2022): 93–103. http://dx.doi.org/10.58506/ajstss.v1i2.11.

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Natural language is a crucial tool to facilitate communication in our day-to-day activities. This can be achieved either in text or speech forms. Natural language processing (NLP) involves making computers understand and process natural language. NLP has enhanced the way humans interact with computers, from having computers use speech to talk to humans as well as having computers translate human speech. Apart from speech, computers also create and understand sentences in natural language in a process called morphological analysis. Morphological analysis is an important part in natural language processing, being applied as a preprocessing step in most NLP tasks. Morphological analysis consists of four subtasks, that is, lemmatization, part-of-speech (POS) tagging, word segmentation and stemming. In this paper, we explore in detail each of these tasks of morphological analysis. We then evaluate the techniques used in this NLP field. Finally, we give a summary of the results of each of these techniques.

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Lewinski, Nastassja A., and Bridget T. McInnes. "Using natural language processing techniques to inform research on nanotechnology." Beilstein Journal of Nanotechnology 6 (July 1, 2015): 1439–49. http://dx.doi.org/10.3762/bjnano.6.149.

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Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

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Park, Kyung-Mi, Han-Cheol Cho, and Hae-Chang Rim. "Utilizing Various Natural Language Processing Techniques for Biomedical Interaction Extraction." Journal of Information Processing Systems 7, no. 3 (September 30, 2011): 459–72. http://dx.doi.org/10.3745/jips.2011.7.3.459.

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Wilson, Adam. "Natural-Language-Processing Techniques for Oil and Gas Drilling Data." Journal of Petroleum Technology 69, no. 10 (October 1, 2017): 96–97. http://dx.doi.org/10.2118/1017-0096-jpt.

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Xiong, W., D. Litman, and C. Schunn. "Natural Language Processing techniques for researching and improving peer feedback." Journal of Writing Research 4, no. 2 (November 2012): 155–76. http://dx.doi.org/10.17239/jowr-2012.04.02.3.

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Botov, D. S., and Yu D. Klenin. "Approach to Educational Course Comparison Using Natural Language Processing Techniques." Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 17, no. 3 (2017): 5–14. http://dx.doi.org/10.14529/ctcr170301.

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Zhou, Li, Ying Tao, James J. Cimino, Elizabeth S. Chen, Hongfang Liu, Yves A. Lussier, George Hripcsak, and Carol Friedman. "Terminology model discovery using natural language processing and visualization techniques." Journal of Biomedical Informatics 39, no. 6 (December 2006): 626–36. http://dx.doi.org/10.1016/j.jbi.2005.10.006.

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Pandey, Sheela, Sanjay K. Pandey, and Larry Miller. "Measuring Innovativeness of Public Organizations: Using Natural Language Processing Techniques." Academy of Management Proceedings 2015, no. 1 (January 2015): 12025. http://dx.doi.org/10.5465/ambpp.2015.12025abstract.

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Xi, Su Mei. "Application of Natural Language Processing for Information Retrieval." Applied Mechanics and Materials 380-384 (August 2013): 2614–18. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2614.

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Through a comprehensive analysis of using natural language processing in information retrieval, we compared the effects with the various natural language techniques for information retrieval precision in this paper. This is for the tasks of more suitable as well as accurate results of natural language processing.

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T.C., Sandanayake. "Automated Classroom Lecture Note Generation Using Natural Language Processing and Image Processing Techniques." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 5 (October 15, 2019): 1920–26. http://dx.doi.org/10.30534/ijatcse/2019/16852019.

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Abera Hordofa, Bekele, and Shambel Dechasa Degefa. "A Review of Natural Language Processing Techniques: Application to Afan Oromo." International Journal of Computer Applications Technology and Research 10, no. 03 (March 4, 2021): 051–54. http://dx.doi.org/10.7753/ijcatr1003.1001.

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Language is a means of communication and a symbol of national identity. Afan Oromo is one of written and spoken indigenous language in Ethiopia which uses a writing system called Qubee. Natural language processing is automatic or semi-automatic processing of human language that helps computers to understand and process language. NLP techniques involve various linguistic levels to understand and use language. Linguistic levels are an explanatory method for presenting what actually happens within a natural language processing system. This is very important to develop appropriate and desired NLP applications at both higher and lower levels. In this paper, we present a review of techniques, current trends and challenges in NLP application to Afan Oromo.

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Holland, V. Melissa, and Jonathan D. Kaplan. "Natural language processing techniques in computer-assisted language learning: Status and instructional issues." Instructional Science 23, no. 5-6 (November 1995): 351–80. http://dx.doi.org/10.1007/bf00896878.

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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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Almutiri, Talal, and Farrukh Nadeem. "Markov Models Applications in Natural Language Processing: A Survey." International Journal of Information Technology and Computer Science 14, no. 2 (April 8, 2022): 1–16. http://dx.doi.org/10.5815/ijitcs.2022.02.01.

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Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state. The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.

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Rai, Anshuman. "A Review Article on Quantum Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1588–94. http://dx.doi.org/10.22214/ijraset.2022.40103.

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Abstract: Quantum Natural Language Processing is the implementation of NLP algorithms on quantum hardware or alternatively on hybrid quantum-classical hardware. NLP has been a heavily researched and implemented topic of the past few decades and the most recent developments using new techniques and the power of deep learning have made huge strides in the field. But for all this new development, there is a looming possibility of greater achievements in the form of the rising field of quantum computing which is yet to see its potential come to fruition. A gaping hole in the implementation process of NLP systems is the computing power required to train deep learning and Natural Language Processing models which makes the development of such models time consuming and power hungry. The huge leap in parallel computing power that quantum computers provide gives us immense opportunities to accelerate the training of deep and complex models. Such techniques will help organizations with access to quantum hardware to be able to use quantum circuits to either train a complete model or use a classical system like the norm but outsource all of the most computationally heavy part of the process to quantum hardware which will provide exponential speed up to the development of conversational AI models. Keywords: Quantum computing, Natural Language Processing, Quantum Machine Learning, Quantum Natural Language Processing, Noisy Intermediate-Scale Quantum systems, Lambeq, hybrid classical-quantum systems, DisCoCat

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Kanagavalli, R., and Bhagyashri R. Hanji. "A Survey of Deep Learning Techniques in Natural Language Processing Applications." Journal of Computer Science Engineering and Software Testing 06, no. 02 (July 14, 2020): 24–29. http://dx.doi.org/10.46610/jocses.2020.v06i02.004.

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Honjo, Seiichiro, and Tomohiro Ito. "Forecasting the Attractiveness of Crowdsourced Ideas Using Natural Language Processing Techniques:." Japan Marketing Journal 40, no. 3 (January 7, 2021): 31–44. http://dx.doi.org/10.7222/marketing.2021.005.

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Houssein, Essam H., Rehab E. Mohamed, and Abdelmgeid A. Ali. "Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review." IEEE Access 9 (2021): 140628–53. http://dx.doi.org/10.1109/access.2021.3119621.

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Houssein, Essam H., Rehab E. Mohamed, and Abdelmgeid A. Ali. "Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review." IEEE Access 9 (2021): 140628–53. http://dx.doi.org/10.1109/access.2021.3119621.

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Perazzoli, Simone, José Pedro de Santana Neto, and Milton José Mathias Barreto de Menezes. "Systematic analysis of constellation-based techniques by using Natural Language Processing." Technological Forecasting and Social Change 179 (June 2022): 121674. http://dx.doi.org/10.1016/j.techfore.2022.121674.

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Ali, Irfan, Nimra Mughal, Zahid Hussain Khan, Javed Ahmed, and Ghulam Mujtaba. "Resume Classification System using Natural Language Processing and Machine Learning Techniques." Mehran University Research Journal of Engineering and Technology 41, no. 1 (January 1, 2022): 65–79. http://dx.doi.org/10.22581/muet1982.2201.07.

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The selection of a suitable job applicant from the pool of thousands applications is often daunting job for an employer. The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process. Moreover, the automation of this process can significantly expedite and transparent the applicants’ screening process with mere human involvement. This experimental study presents an automated NLP and ML-based RCS that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of RCS and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP and ML techniques for RCS, the extracted features were evaluated on nine ML classification models namely Support Vector Machine - SVM (Linear, SGD, SVC and NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The Term-Frequency-Inverse-Document-Frequency (TF-IDF) feature representation scheme was proved suitable for RCS. The developed models were evaluated using the Confusion Matrix, F-Score, Recall, Precision, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume classification task, the SVM class of Machine Learning classifiers performed better on the study dataset of over nine hundred sixty plus parsed resumes with more than 96% accuracy. The promising results suggest that NLP and ML techniques employed in this study could be used for developing an efficient RCS.

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A. Nwafor, Chidinma, and Ikechukwu E. Onyenwe. "An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques." International Journal on Natural Language Computing 10, no. 02 (April 30, 2021): 1–10. http://dx.doi.org/10.5121/ijnlc.2021.10201.

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Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.

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Salloum, Said, Tarek Gaber, Sunil Vadera, and Khaled Shaalan. "Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey." Procedia Computer Science 189 (2021): 19–28. http://dx.doi.org/10.1016/j.procs.2021.05.077.

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Babu, Gumduboina Seshu. "Content Based Page Ranking by using some Natural Language Processing Techniques." International Journal for Research in Applied Science and Engineering Technology 7, no. 1 (January 31, 2019): 752–56. http://dx.doi.org/10.22214/ijraset.2019.1117.

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Ogbuju, E., and G. N. Obunadike. "Information Extraction from Electronic Medical Records using Natural Language Processing Techniques." Journal of Applied Sciences and Environmental Management 24, no. 6 (July 17, 2020): 1027–33. http://dx.doi.org/10.4314/jasem.v24i6.13.

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Patients share key information about their health with medical practitioners during clinic consultations. These key information may include their past medications and allergies, current situations/issues, and expectations. The healthcare professionals store this information in an Electronic Medical Record (EMR). EMRs have empowered research in healthcare; information hidden in them if harnessed properly through Natural Language Processing (NLP) can be used for disease registries, drug safety, epidemic surveillance, disease prediction, and treatment. This work illustrates the application of NLP techniques to design and implement a Key Information Retrieval System (KIRS framework) using the Latent Dirichlet Allocation algorithm. The cross-industry standard process for data mining methodology was applied in an experiment with an EMR dataset from PubMed todemonstrate the framework. The new system extracted the common problems (ailments) and prescriptions across the five (5) countries presented in the dataset. The system promises to assist health organizations in making informed decisions with the flood of key information data available in their domain. Keywords: Electronic Medical Record, BioNLP, Latent Dirichlet Allocation

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Sun, Shiliang, Chen Luo, and Junyu Chen. "A review of natural language processing techniques for opinion mining systems." Information Fusion 36 (July 2017): 10–25. http://dx.doi.org/10.1016/j.inffus.2016.10.004.

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Quintana, Manuel A., Ramón R. Palacio, Gilberto Borrego Soto, and Samuel González-López. "Agile Development Methodologies and Natural Language Processing: A Mapping Review." Computers 11, no. 12 (December 7, 2022): 179. http://dx.doi.org/10.3390/computers11120179.

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Agile software development is one of the most important development paradigms these days. However, there are still some challenges to consider to reduce problems during the documentation process. Some assistive methods have been created to support developers in their documentation activities. In this regard, Natural Language Processing (NLP) can be used to create various related tools (such as assistants) to help with the documentation process. This paper presents the current state-of-the-art NLP techniques used in the agile development documentation process. A mapping review was done to complete the objective, the search strategy is used to obtain relevant studies from ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Willey. The search results after inclusion and exclusion criteria application left 47 relevant papers identified. These papers were analyzed to obtain the most used NLP techniques and NLP toolkits. The toolkits were also classified by the kind of techniques that are available in each of them. In addition, the behavior of the research area over time was analyzed using the relevant paper found by year. We found that performance measuring methods are not standardized, and, in consequence, the works are not easily comparable. In general, the number of related works and its distribution per year shows a growing trend of the works related to this topic in recent years; this indicates that the adoption of NLP techniques to improve agile methodologies is increasing.

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