Dictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book‐dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation of GDEX are very strenuous and require a lot of time for committing the manual process. In this regard, this paper focuses on two major tasks, i.e., the development of labelled corpora for top 3K English words through the usage of distant supervision approach and devising a state‐of‐the‐art artificial intelligence‐based automated procedure for discriminating Good Dictionary EXamples from the bad ones. The proposed methodology involves a suite of five machine learning (ML) and five word embedding‐based deep learning (DL) architectures. A thorough analysis of the results shows that GDEX elicitation can be done by both ML and DL models; however, DL‐based models show a trivial improvement of 3.5% over the conventional ML models. We find that the random forests with parts‐of‐speech information and word2vec‐based bidirectional LSTM are the most optimal ML and DL combinations for automated GDEX elicitation; on the test set, these models, respectively, secured a balanced accuracy of 73% and 77%.

Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design. Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

In today’s digital age, customer feedback, particularly gathered from various sources like mobile application reviews, has emerged as a critical resource for service-providing organizations to gain valuable insights into their customers’ experiences. As the key objective of service-providing organizations is to facilitate their customers with better services, customer feedback or opinion is a vital resource for such organizations to improve and enhance their services for the betterment of their customers. Explicitly mentioned opinions have been widely studied in research, while a significant gap exists in addressing implicitly described views. Furthermore, most existing research focuses on product-oriented corpora, emphasizing specific product aspects and features. This article presents a novel study on performing end-to-end aspect-based sentiment analysis (ABSA) by extracting implicit opinion terms, categorizing them, and assigning polarity to each term from mobile app reviews in English. Through this study, we developed a domain-specific, service-oriented, and aspect-based annotated dataset and introduced a novel two-step hybrid approach. The first step involves extracting multiple opinion terms using a rule-based approach. The second step employs machine learning and deep learning algorithms to classify the extracted opinion terms into general aspect categories. This two-step approach effectively addresses the double-implicit problem commonly encountered in the previouswork on implicit aspects and opinion mining. In addition to traditional machine learning and deep learning models, we fine-tuned BERT to carry out the ABSA task. This approach utilized a pipeline method, where each task’s output serves as the subsequent task’s input, ensuring a seamless flow of information and improved performance. This multi-step pipeline begins with the extracted opinion terms classification into aspect categories and ends with the assignment of sentiment polarity. Experiments with a hold-out test set for the first step (opinion term extraction using a rule-based approach) achieved an accuracy and precision score of 81.4% and an F1 score of 0.99 %, outperforming several baselines. Further experiments with a range of machine learning and deep learning algorithms for classifying extracted opinion terms into general aspect categories yielded accuracy scores ranging from 0.68% to 0.74 % and F1 scores ranging from 0.23% to 0.28%. Experiments on sentiment classification using various machine learning algorithms showed accuracy ranges from 0.58% to 0.68% and F1 scores from 0.47% to 0.49%. This two-step approach for implicit opinion term, aspect extraction, and classification outperforms many baseline systems. By leveraging BERT’s contextual understanding and fine-tuning it for our specific domain, we significantly improved the accuracy and robustness of our aspect-based sentiment analysis. This approach effectively captured both explicit and implicit opinions from mobile app reviews. Specifically, our method achieved an accuracy of 0.80% and an F1 score of 0.78% for aspect categorization, and an accuracy of 0.79% and an F1 score of 0.70% for sentiment classification, demonstrating substantial improvements over traditional methods.

E-learning has established itself as a new alternative to conventional learning styles to accomplish the goal of education and learning for everyone. The classroom learning style is based on a “one size fits all” approach because, in a typical classroom environment, an instructor has to deal with several students at the same time. A similar problem goes along with the traditional learning management systems in which every student must learn the course devised by the instructor, within a specific timeline and achieve specific objectives, despite students’ preferences and capabilities. The recent global pandemic has pushed educational institutes worldwide to recourse to an “online-only” mode of education and teaching delivery which has raised many challenges that need to be addressed. The existing e-learning systems fail to fulfill the expectations of a learner or an instructor in certain ways including the style of content delivery, mode of teaching, an adaptation of learner style that doesn’t match with the teaching style, the content type, and above all the e-learning lacks to provide the e-mentoring capability to deal with the challenges a learner and an instructor’s face during the learning process. This research aims to develop personalized learning by incorporating an intelligent e-mentor. The proposed e-mentor-based learning model is capable of customizing a course for individuals by automatically adapting it to their unique learning styles, preferences, abilities, existing knowledge, and expectations from the course. The outcome of the proposed model shows that e-mentoring not only increased learner satisfaction but also enhanced the learning process making it a preferred choice.

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.

Chatbots or conversational agents are the most projecting and widely employed artificial assistants on online social media. These bots converse with the humans in audio, visual, or textual formats. It is quite intelligible that users are keen interested in the swift and relatedly correct information for their hunt in pursuit of desired product, such that their precious time is not wasted through surfing multiple websites and business portals. In this paper, we present a novel incremental approach for building a chatbot for fashion brands based on the semantic web. We organized a dataset of 5,000 question and answers of top- 10 brands in the fashion domain, which covers the information about new arrivals, sales, packages, discounts, exchange/return policies, etc. We have also developed a dialogue interface for querying the system. The results generated against the queries are thoroughly evaluated on the criteria of time, context, history, duration, turns, significance, relevance, and fall back questions.