With the advancement of scientific collaboration in the 20^th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co‐authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co‐authorship network of the researcher up to the 2^nd level was developed. Then the co‐authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web‐based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human‐Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively.

A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document’s content that can reflect the authors’ main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors’ provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.

Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today’s information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title keywords combination outperforms other combinations with an F-measure score of 0.88.