Lo spagnolo che traduce nella storia Lettura critica di El tabaco que fumaba Plinio

This chapter aims to analyse the hypotheses and theses of El tabaco que fumaba Plinio. Escenas de la traducción en España y América: relatos, leyes y reflexiones sobre los otros, edited by Marietta Gargatagli and Nora Catelli and published in 1998 by Ediciones del Serbal (Barcelona). The book is an anthology in Spanish and not only does it include texts translated into Spanish, but multiple documents (premises, laws, reflections, myths) on the act of translating. According to the editors, the long history of the relationship between the Spaniards and the Other highlights from the outset different strategies of appropriation, domestication, acclimatisation and rewriting which also drew on the domination, exclusion and omission of the Other’s voice.

Cross - Language based Multi-Document Summarization Model using Machine Learning Technique

Cross-Language Multi-document summarization (CLMDS) process produces a summary generated from multiple documents in which the summary language is different from the source document language. The CLMDS model allows the user to provide query in a particular language (e.g., Tamil) and generates a summary in the same language from different language source documents. The proposed model enables the user to provide a query in Tamil language, generate a summary from multiple English documents, and finally translate the summary into Tamil language. The proposed model makes use of naïve Bayes classifier (NBC) model for the CLMDS. An extensive set of experimentation analysis was performed and the results are investigated under distinct aspects. The resultant experimental values ensured the supremacy of the presented CLMDS model.

Performance and acceptance when using tablets as a multiple document learning tool: do application and guidance matter?

This study addressed whether an application adapted to working with multiple documents implemented in an iPad Pro tablet would promote students’ multiple document comprehension and acceptance of tablets as a multiple document learning tool relative to controls who used a traditional application adapted to sequential reading of single documents. Results indicated that students using the multiple document reading application outperformed the control students in terms of comprehension and also worked more efficiently on the assigned multiple document task, but only if given explicit guidance in selecting, organizing, and integrating information by utilizing the functions of the application. Still, after task completion, the more effective and efficient students guided in using the functions of the multiple document reading application displayed much less acceptance of tablets as a multiple document learning tool than did the control students. We discuss possible explanations for this intriguing performance-acceptance paradox and suggest some avenues for future research in this area.

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods. Doi: 10.28991/esj-2021-01268 Full Text: PDF