Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets

Abstract

Keyword extraction is a critical task that enables various applications, including text classification, sentiment analysis, and information retrieval. However, the lack of a suitable dataset for semantic analysis of keyword extraction remains a serious problem that hinders progress in this field. Although some datasets exist for this task, they may not be representative, diverse, or of high quality, leading to suboptimal performance, inaccurate results, and reduced efficiency. To address this issue, we conducted a study to identify a suitable dataset for keyword extraction based on three key factors: dataset structure, complexity, and quality. The structure of a dataset should contain real-time data that is easily accessible and readable. The complexity should also reflect the diversity of sentences and their distribution in real-world scenarios. Finally, the quality of the dataset is a crucial factor in selecting a suitable dataset for keyword extraction. The quality depends on its accuracy, consistency, and completeness. The dataset should be annotated with high-quality labels that accurately reflect the keywords in the text. It should also be complete, with enough examples to accurately evaluate the performance of keyword extraction algorithms. Consistency in annotations is also essential, ensuring that the dataset is reliable and useful for further research.

Improving Publishing: Extracting Keywords and Clustering Topics

“…After all data points are assigned to clusters, the centroids are updated by computing the mean of all data points assigned to each cluster. The assignment and update steps are repeated iteratively until convergence, where the centroids no longer change significantly or a maximum number of iterations is reached [28], [29], [30], [31], [32].…”

“…K-means clustering is a versatile and widely used algorithm for partitioning data into clusters, with applications across different domains and industries. It provides an efficient and interpretable way to explore data structure and discover underlying patterns, such as customer segmentation, image compression, document clustering, and anomaly detection [28], [29], [30], [31], [32].…”

Machine Learning Model for Automated Assessment of Short Subjective Answers

“…Furthermore, there is a need for developing robust methods to handle outliers, exceptions, and edge cases that are often encountered in short answer assessment. This requires careful consideration of the characteristics of short answers and the design of models and algorithms that can handle such situations effectively [36][37][38][39][40]. To overcome these challenges, potential solutions include utilizing data augmentation techniques to generate more diverse training data, developing novel algorithms and models specifically tailored for short answer assessment, and leveraging domain-specific knowledge and expertise to enhance the performance of machine learning models.…”

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

“…These industry researchers have been found to trust a variety of ML techniques, including the most widely used recurrent neural networks (RNNs), artificial neural networks (ANNs), and support vector machine (SVM), with the infrequent AI method involving fuzzy inference systems (FISs). Many researchers have shown success using ML, AI, and smart technology in water-based applications to optimize modeling techniques [24]. While these achievements have been acknowledged, ML and AI applications do not come without constraints that need to be addressed before general execution.…”