Natural Language Processing

Fanni, Salvatore Claudio; Febi, Maria; Aghakhanyan, Gayane; Neri, Emanuele

doi:10.1007/978-3-031-25928-9_5

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…The techniques associated with NLP enable the extraction of meaningful insights from vast amounts of unstructured textual data, facilitating efficient information retrieval, analysis, and decision-making processes [31]. NLP finds diverse applications in the e-commerce domain, including the development of chatbots, virtual assistants, language translation services, content summarization tools, and sentiment analysis [32]. Through its capability to harness the power of language, NLP contributes to the creation of intelligent systems that understand and communicate with humans in a natural and intuitive manner.…”

Section: Natural Language Processing and Advanced Language Modelsmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

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Section: Natural Language Processing and Advanced Language Modelsmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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“…Although the recent spike in popularity of deep learning, conventional machine learning approaches are still essential for analysing medical tabular data [8], [9], even while deep learning dominates sectors like image processing [10] and natural language processing [11]. Deep learning requires significant computer resources and a large dataset to achieve success, whereas standard machine learning methods such as XGBoost can provide high performance with less computational burden.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique

Irmawati,

Herdit Juningsih,

Yanto

2024

MEDINFTech

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This study presents a predictive modeling framework for osteoporosis risk assessment using ensemble techniques, specifically XGBoost and Bagging. Leveraging a dataset comprising comprehensive health factors influencing osteoporosis development, including demographic details, lifestyle choices, medical history, and bone health indicators, the aim is to facilitate accurate identification of individuals at risk. The dataset consists of 1958 samples, evenly distributed between osteoporosis-positive and osteoporosis-negative cases. The methodology involves the separation of features and labels, followed by data splitting into training and testing sets. XGBoost, a powerful gradient boosting algorithm, is employed as the base estimator within a Bagging ensemble, enhancing predictive accuracy and generalization. The model is trained on the training set and evaluated using cross-validation techniques to ensure robustness and mitigate overfitting. The results of the classification report demonstrate promising performance metrics, with an overall accuracy of 88% on the test set. Precision and recall scores indicate strong predictive capabilities, particularly in correctly identifying osteoporosis-positive cases. The novel integration of XGBoost within a Bagging ensemble provides an innovative approach to osteoporosis risk prediction, harnessing the strengths of both algorithms to improve model performance. This research contributes to the advancement of osteoporosis management and prevention strategies by providing a reliable tool for early risk assessment. The combination of machine learning techniques with comprehensive health data offers a valuable approach to personalized healthcare, enabling targeted interventions and optimized resource allocation. Ultimately, this study aims to enhance patient outcomes and reduce the burden of osteoporosis-related morbidity and mortality.

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“…Depending on the type of data, the datasets can be further classified into text, image, and audio datasets. Text datasets are essential in Natural Language Processing (NLP) (Fanni et al, 2023) tasks such as sentiment analysis (Kapočiūtė-Dzikienė & Salimbajevs, 2022;Shaik et al, 2023;Mercha & Benbrahim, 2023), text classification (Štrimaitis et al, 2022;Palanivinayagam et al, 2023) and semantic analysis (Maulud et al, 2021). These main types of datasets are discussed by Gong et al (2023).…”

Section: Introductionmentioning

confidence: 99%

Educational Data Mining and Learning Analytics: Text Generators Usage Effect on Students’ Grades

Pliuskuvienė,

Radvilaitė,

Juodagalvytė

et al. 2024

New Trends in Computer Sciences

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Today, various types of data are constantly growing, so they can be used for different purposes. In this investigation, educational data has been analyzed to determine the influence of assessment on student knowledge. The newly collected dataset has been prepared and statistically analyzed. The dataset consists of open-question answers collected on one study subject during the midterm exam at Vilnius Gediminas Technical University. The results of the statistical analysis have shown that by using the text generators, students obtained higher grades by paraphrasing the answers to the questions in good quality. Furthermore, research has shown which types of questions are more difficult for students to answer without additional material and using text generation tools. It can be useful for lecturers planning course assessment tasks.

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Natural Language Processing

Cited by 12 publications

References 32 publications

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique

Educational Data Mining and Learning Analytics: Text Generators Usage Effect on Students’ Grades

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