Hei‐Chia Wang, Ya‐lin Chou and Jiunn‐Liang Guo
The paper's aim is to propose a core journal decision method, called the local impact factor (LIF), which can evaluate the requirements of the local user community by combining…
Abstract
Purpose
The paper's aim is to propose a core journal decision method, called the local impact factor (LIF), which can evaluate the requirements of the local user community by combining both the access rate and the weighted impact factor, and by tracking citation information on the local users' articles.
Design/methodology/approach
Many institutions with a limited budget can subscribe only to the most valuable journals for their users. The importance of a journal to a local community can be calculated in many ways. This paper takes both global and local access frequency and journal citations into consideration. The method of weighted web page link analysis is adopted.
Findings
This paper finds that the weighted page rank may be used efficiently in the core journal decisions. Experimental results demonstrate that the proposed LIF can effectively suggest journals to local users better than existing methods (i.e. impact factor or the local journal rank).
Research limitations/implications
This research requires the determination of the thesis scores, which needs authorisation from the authors. If the scores are not available, the scores may be subjectively assigned or retrieved from the other resources.
Practical implications
A case study in National Cheng Kung University was conducted to show that the LIF can be used to help library managers evaluate the real demands of local community users.
Originality/value
Rather than existing research, this paper focuses on the utilisation and requirements of local community users and also finds the contributions of citation information to be significant and critical.
Details
Keywords
Jiunn-Liang Guo, Hei-Chia Wang and Ming-Way Lai
The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documents – e-books of online library system. The…
Abstract
Purpose
The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documents – e-books of online library system. The main idea mainly aims on automatically identifying the discourse features in order to improving the feature selection process rather than focussing on the size of the corpus.
Design/methodology/approach
The proposed framework intends to automatically identify the discourse segments within e-books and capture proper discourse subtopics that are cohesively expressed in discourse segments and treating these subtopics as informative and prominent features. The selected set of features is then used to train and perform the e-book classification task based on the support vector machine technique.
Findings
The evaluation of the proposed framework shows that identifying discourse segments and capturing subtopic features leads to better performance, in comparison with two conventional feature selection techniques: TFIDF and mutual information. It also demonstrates that discourse features play important roles among textual features, especially for large documents such as e-books.
Research limitations/implications
Automatically extracted subtopic features cannot be directly entered into FS process but requires control of the threshold.
Practical implications
The proposed technique has demonstrated the promised application of using discourse analysis to enhance the classification of large digital documents – e-books as against to conventional techniques.
Originality/value
A new FS technique is proposed which can inspect the narrative structure of large documents and it is new to the text classification domain. The other contribution is that it inspires the consideration of discourse information in future text analysis, by providing more evidences through evaluation of the results. The proposed system can be integrated into other library management systems.
Details
Keywords
Yeou-Jiunn Chen and Jiunn-Liang Wu
Articulation errors substantially reduce speech intelligibility and the ease of spoken communication. Moreover, the articulation learning process that speech-language pathologists…
Abstract
Purpose
Articulation errors substantially reduce speech intelligibility and the ease of spoken communication. Moreover, the articulation learning process that speech-language pathologists must provide is time consuming and expensive. The purpose of this paper, to facilitate the articulation learning process, is to develop a computer-aided articulation learning system to help subjects with articulation disorders.
Design/methodology/approach
Facial animations, including lip and tongue animations, are used to convey the manner and place of articulation to the subject. This process improves the effectiveness of articulation learning. An interactive learning system is implemented through pronunciation confusion networks (PCNs) and automatic speech recognition (ASR), which are applied to identify mispronunciations.
Findings
Speech and facial animations are effective for assisting subjects in imitating sounds and developing articulatory ability. PCNs and ASR can be used to automatically identify mispronunciations.
Research limitations/implications
Future research will evaluate the clinical performance of this approach to articulation learning.
Practical implications
The experimental results of this study indicate that it is feasible for clinically implementing a computer-aided articulation learning system in learning articulation.
Originality/value
This study developed a computer-aided articulation learning system to facilitate improving speech production ability in subjects with articulation disorders.