Location metonymy resolution is a study that deals with locations being used in a non-literal way that create problems in several natural language processing tasks such as Named entity recognition ...and Geographical parsing. Many studies were conducted attempting to accurately classify whether the location is used literally or metonymically, however, most of the approaches that performed well had to employ a considerable amount of resources along with complex machine learning models; those that reduced the resources experienced a decline in performance due to data sparseness. This study proposes a novel feature selection approach that uses bag-of-words and augments it with GloVe embeddings to obtain features that can be recognized based on the context of the sentence. We then implement a minimalist deep learning model making the entire classification task as light as possible. The study found that relying solely on the given datasets to identify features without depending on other external resources can achieve remarkable results despite the small size of the datasets. The results obtained from evaluating our method compared to the state-of-the-art methods show that eliminating noise based on the context notwithstanding the usage of low-cost resources has outperformed all of the previous methods with an accuracy of 99.2% on the WIMCOR dataset.
Artificial intelligence technology is becoming increasingly essential to education. The outbreak of COVID-19 in recent years has led many schools to launch online education. Automated online ...assessments have become a hot topic of interest, and an increasing number of researchers are studying Automated Essay Scoring (AES). This work seeks to summarise the characteristics of current AES systems used in English writing assessment, identify their strengths and weaknesses, and finally, analyse the limits of recent studies and research trends. Search strings were used to retrieve papers on AES systems from 2018 to 2023 from four databases, 104 of which were chosen to be potential to address the posed research aims after study selection and quality evaluation. It is concluded that the existing AES systems, although achieving good results in terms of accuracy in specific contexts, are unable to meet the needs of teachers and students in real teaching scenarios. The improvements of these systems relate to the scalability of the system for assessing different topics or styles of the essays, the accuracy of the model's predicted scores, as well as the reliability of outcomes: improving the robustness of AES models with some adversarial inputs, the richness of AES system functionality, and the development of AES assist tools.
Adaptive support within a learning environment is useful because most learners have different personal characteristics such as prior knowledge, learning progress, and learning preferences. This study ...reviews various implementation of adaptive feedback, based on the four adaptation characteristics: means, target, goal, and strategy. This review focuses on 20 different implementations of feedback in a computer-based learning environment, ranging from multimedia web-based intelligent tutoring systems, dialog-based intelligent tutoring systems, web-based intelligent e-learning systems, adaptive hypermedia systems, and adaptive learning environment. The main objective of the review is to compare computer-based learning environments according to their implementation of feedback and to identify open research questions in adaptive feedback implementations. The review resulted in categorizing these feedback implementations based on the students’ information used for providing feedback, the aspect of the domain or pedagogical knowledge that is adapted to provide feedback based on the students’ characteristics, the pedagogical reason for providing feedback, and the steps taken to provide feedback with or without students’ participation. Other information such as the common adaptive feedback means, goals, and implementation techniques are identified. This review reveals a distinct relationship between the characteristics of feedback, features of adaptive feedback, and computer-based learning models. Other information such as the common adaptive feedback means, goals, implementation techniques, and open research questions are identified.
•We identified different knowledge base modelling and manipulation techniques based on 4 categories.•Compared knowledge base modelling and manipulation technologies based on their underlying ...theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages.•We discussed the relevance of knowledge-based business.•We proposed a promising technique for knowledge-based business management and other knowledge related applications.
A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000–2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.
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