An improved joint model: POS tagging and dependency parsing

Dependency parsing is a way of syntactic parsing and a natural language that automatically analyzes the dependency structure of sentences, and the input for each sentence creates a dependency graph. Part-Of-Speech (POS) tagging is a prerequisite for dependency parsing. Generally, dependency parsers do the POS tagging task along with dependency parsing in a pipeline mode. Unfortunately, in pipeline models, a tagging error propagates, but the model is not able to apply useful syntactic information. The goal of joint models simultaneously reduce errors of POS tagging and dependency parsing tasks. In this research, we attempted to utilize the joint model on the Persian and English language using Corbit software. We optimized the model's features and improved its accuracy concurrently. Corbit software is an implementation of a transition-based approach for word segmentation, POS tagging and dependency parsing. In this research, the joint accuracy of POS tagging and dependency parsing over the test data on Persian, reached 85.59% for coarse-grained and 84.24% for fine-grained POS. Also, we attained 76.01% for coarse-grained and 74.34% for fine-grained POS on English

Customer knowledge management in software development: a descriptive field survey

Customer Knowledge (CK) plays an important role in the production of high quality and innovative software products. However, there has been little comprehensive academic research on the ‘enablers’ of customer-specific knowledge. Therefore, study aims to analyze Customer Knowledge Management (CKM) ‘enablers’ for enterprise software development companies. Survey questionnaires were distributed to software companies and results showed that most firms focus their efforts more on ‘Technological Infrastructure’ and less on ‘Human’ and ‘Organizational’ CKM enablers. Results demonstrated low positive percent ratings for ‘Human Antecedents’ (Individual Competences & Skills) and ‘Organizational’ enablers (‘Customer Involvement’, CKM ‘Strategy Development’ and ‘Training’). This study contributes to the CKM domain by revealing essential elements that better enable enterprise software development firms to enhance software quality and produce innovative products. The author recommends that software companies place greater emphasis on ‘Human’ and ‘Organizational’ enablers for the successful implementation of CKM strategies

Reliability-based fuzzy clustering ensemble

In the clustering ensemble the quality of base-clusterings influences the consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble. In this paper, we propose a new fuzzy clustering ensemble framework without access to the features of data-objects based on fuzzy cluster-level weighting. The reliability of each fuzzy cluster is computed based on estimation of its unreliability, and is considered as its weight in the ensemble. The unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion. In our framework, the final clustering is produced by two types of consensus functions: (1) a reliability-based weighted fuzzy co-association matrix is constructed from the base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering or K-means is applied over the matrix to produce the final clustering. (2) a new graph based fuzzy consensuses function. The graph based consensus function has linear time complexity in the number of data-objects. Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness. © 2020 Elsevier B.V

Toward a customer knowledge management model for enhancing enterprise software quality

Many previous research studies on software quality enhancement have only focused on the technical aspects of software quality, such as reliability, maintainability and functionality. However, the nature of enterprise software requires additional focus on transferring and integration of customer knowledge, for customization, enhancement, maintenance and training. As customers are seen as one of the most important stakeholders in software projects, it would appear that the current use of customer knowledge in software development is insufficient. This study concentrates on investigating Human, Organizational and Technological factors of customer knowledge management (CKM) in order to enhance the quality of enterprise software within software companies. After investigating CKM factors in the literature, a Technique for the Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to rank these factors regarding the importance of CKM development in enterprise software companies, in order to improve software product quality. The weight and priority of the factors were determined by 31 software development company experts. Based on the highest priority factors, a theoretical model was developed. This study proposes a fundamental model that can be used as a guideline for successful CKM applications, within enterprise software development companies, to improve software quality

Elite fuzzy clustering ensemble based on clustering diversity and quality measures

In spite of some attempts at improving the quality of the clustering ensemble methods, it seems that little research has been devoted to the selection procedure within the fuzzy clustering ensemble. In addition, quality and local diversity of base-clusterings are two important factors in the selection of base-clusterings. Very few of the studies have considered these two factors together for selecting the best fuzzy base-clusterings in the ensemble. We propose a novel fuzzy clustering ensemble framework based on a new fuzzy diversity measure and a fuzzy quality measure to find the base-clusterings with the best performance. Diversity and quality are defined based on the fuzzy normalized mutual information between fuzzy base-clusterings. In our framework, the final clustering of selected base-clusterings is obtained by two types of consensus functions: (1) a fuzzy co-association matrix is constructed from the selected base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering is applied as consensus function over the matrix to construct the final clustering. (2) a new graph based fuzzy consensus function. The time complexity of the proposed consensus function is linear in terms of the number of data-objects. Experimental results reveal the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria on various standard datasets. © 2018, Springer Science+Business Media, LLC, part of Springer Nature