Using bag-of-concepts to improve the performance of support vector machines in text categorization

This paper investigates the use of concept-based representations for text categorization. We introduce a new approach to create concept-based text representations, and apply it to a standard text categorization collection. The representations are used as input to a Support Vector Machine classifier, and the results show that there are certain categories for which concept-based representations constitute a viable supplement to word-based ones. We also demonstrate how the performance of the Support Vector Machine can be improved by combining representations

Applications of Multi-view Learning Approaches for Software Comprehension

Program comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software system. Each of these views contain unique and complementary information; together which can more accurately describe the data. In this paper, we investigate various techniques for combining different sources of information to improve the performance of a program comprehension task. We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. The experiments conducted on 10 relatively large Java software systems show that by fusing knowledge from different views, we can guarantee a lower bound on the quality of the modularization and even improve upon it. We proceed by integrating different sources of information to give a set of high-level recommendations as to how to refactor the software system. Furthermore, we demonstrate how learning a joint subspace allows for performing cross-modal retrieval across views, yielding results that are more aligned with what the user intends by the query. The multi-view approaches outlined in this paper can be employed for addressing problems in software engineering that can be encoded in terms of a learning problem, such as software bug prediction and feature location

Text mining of biomedical literature: discovering new knowledge

Biomedical literature is increasing day by day. The present scenario shows that the volume of literature regarding “coronavirus” has expanded at a high rate. In this study, text mining technique has been employed to discover something new from the published literature. The main objectives of this study are to show the growth of literature (Jan-Jun, 2020), extract document section, identify latent topics, find the most frequent word, represent the bag of words, and the hierarchical clustering. We have collected 16500 documents from PubMed. This study finds most number of documents (11499) belong to May and June. We explore “betacoronavirus” as the leading document section (3837); “covid” (29890) as the most frequent word in the abstracts; and positive-negative weights of topics. Further, we measure the term frequency (TF) of a document title in the bag of words model. Then we compute a hierarchical clustering of document titles. It reveals that the lowest distance the selected cluster (C133) is 0.30. We also have made a discussion over future prospects and mentioned that this paper can be useful to researchers and library professionals for knowledge management

Support Vector Machines and Kernel Functions for Text Processing

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The “kernel trick”, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions