Abstract. Rapid identification of content structures in a scientific paper is of great importance particularly for those who actively engage in frontier research. This paper presents a multi terms of classification of r behind this approach is based on an observation that no single classifier is the best performer for classifyi our approach learns which classifiers for those categories and apply only the right classifier for classifying a given category. This paper obtain the category-classifier of the corresponding classifier using full This approach has been evaluated for identi categories on sentences collected from ACL experimental results show that the multi improve the classification performance over multi Keywords: acl-arc; classification strategies classification; rhetorical sentence categorization IntroductionKeeping abreast of the state researchers and reading new papers could be a daunting task proliferation of scientific publication. considered more effective is to provide readers with structured information that is extracted from a scientific paper. This structured information is represented as Rhetorical Document Profile (RDP) [1]. that readers want to know from a paper so that readers can identify the relevance of a paper just by reading its consisting of rhetorical slots. Each slot contains a collection of specific rhetorical category. Rhetorical sentence classification is the most important and major step in creating an RDP. This process is also known J. ICT Res. Appl., Vol. 7, No. 3, 2013, 235-249 Rapid identification of content structures in a scientific paper is of great importance particularly for those who actively engage in frontier research.a multi-classifier approach to identify such structures in terms of classification of rhetorical sentences in scientific papers. The idea approach is based on an observation that no single classifier is the best performer for classifying all rhetorical categories of sentences. Therefore, which classifiers are good at what categories, assign the classifiers for those categories and apply only the right classifier for classifying a This paper employs k-fold cross validation over training data to classifier mapping and then re-learn the classification model of the corresponding classifier using full training data on that particular category. This approach has been evaluated for identifying sixteen different rhetorical categories on sentences collected from ACL-ARC paper collection. The experimental results show that the multi-classifier approach can significantly improve the classification performance over multi-label classifiers.; classification strategies; multiclass approach; multi-label sentence categorization; scientific papers.Keeping abreast of the state-of-the-art of research topics is a must for researchers and reading new papers could be a daunting task with current fic publication. An alternative solution that can be considered more effective is to provide readers with structured information that is extracted from a scientific paper. This s...
<p>Islam as an unperceived religion from an essentialist perpective, beause it is transhistorical. The reality shows that the expression of Islam in one particular geographical context is a result of the interplays between Islamic teachings and local culture. This is a qualitative studyon Minangkabau customs and culture, with data collected by reviewing documents, both in the form of books, and articles. In addition, interviews were conducted with a number of Minang figures, and added to the Minang residents experiences. The results showed that the Minangkabau customs and culture had acculturated with Islam since it was introduced to the Minang region around the 8<sup>th</sup> century AD. Before its introduction, there were customs and cultures based on their habits. This acculturation occurs peacefully, therefore, the decisions of the traditional leaders do not cause turmoil and resistance from adat stakeholders in the region. Acculturation of Minang customs and culture with Islam takes the form of synthetism, while <em>adat</em> adapts to its teachings. When Islam with Minang customs and culture blend into one, changes occur in three forms. <em>Firstly</em>, when the custom is not in accordance with the Islamic teachings, it is adjusted, as illustrated in the Minang customary philosophy which reads, “<em>Adat basandi alua jo patuik, alua jo patuik basandi bana, bana badiri sandirinyo</em>” changed into “<em>Adat basandi</em><em> Syara’, Syara’ basandi </em><em>Kitabullah</em>”. <em>Secondly</em>, both customs and culture, which are in line with Islam remain preserved, such as the principle of deliberation (<em>musyawarah</em>) and consensus (<em>mufakat</em>). <em>Thirdly</em>, it led to the promulgation of Islam in a new culture that has not existed before, such as the <em>Khatam Al-</em><em>Qur</em><em>’an</em><em> </em>ceremony for children.</p>
Time constraints often lead a reader of scientific paper to read only the title and abstract of the paper, but reading these parts is often ineffective. This study aims to extract information automatically in order to help the readers get structured information from a scientific paper. The information extraction is done by rhetorical classification of each sentence in a scientific paper. Rhetoric information is the intention to be conveyed to the reader by the author of the paper. This research used corpusbased approach to build rhetorical classifier. Since there was a lack of rethorical corpus, we constructed our own corpus, which is a collection of sentences that have been labeled with rhetorical information. Each sentence represented as a vector of content, location, citation, and meta-discourses features. This collection of feature vectors is used to build rhetorical classifiers by using machine learning techniques. Experiments were conducted to select the best learning techniques for rhetorical classifier. Training set consists of 7239 labeled sentences, and the testing set consists of 3638 labeled sentences. We used WEKA (Waikato Environment for Knowledge Analysis) and LibSVM libraries. Learning techniques being considered were Naive Bayes, C4.5, Logistic, Multi-Layer Perceptron, PART, Instance-based Learning, and Support Vector Machines (SVM). The best performers are the SVM and Logistic classifier with accuracy of 0.51. By applying one-against-all strategy, the SVM accuracy can be improved to 0.60.
In order to assist researchers in addressing time constraint and low relevance in using scientific articles, an automatic tailored multi-paper summarization (TMPS) is proposed. In this paper, we extend Teufel’s tailored summary to deal with multi-papers and more flexible representation of user information needs. Our TMPS extracts Rhetorical Document Profile (RDP) from each paper and presents a summary based on user information needs. Building Plan Language (BPLAN) is introduced as a formalization of Teufel’s building plan and used to represent summary specification, which is more flexible representation of user information needs. Surface repair is embedded within the BPLAN for improving the readability of extractive summary. Our experiment shows that the average performance of RDP extraction module is 94.46%, which promises high quality of extracts for summary composition. Generality evaluation shows that our BPLAN is flexible enough in composing various forms of summary. Subjective evaluation provides evidence that surface repair operators can improve the resulting summary readability
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