JOURNALISTS AT WAR     

《Journalism Practice》2013,7(4):492-506

This article looks closely at the issue of unionization among a sample of Arab journalists working in transnational media. Although divided geographically, these media share the same trait of addressing Arab audiences all over the Arab region and indeed the whole world. The main question addressed in this article is how those journalists perceive the role of unions and whether there are differences among those who work in Europe, particularly London, vis-à-vis those in the Gulf. The article is based on interviews with 25 journalists from such outlets, who were asked about their membership and views of journalism unions in their local or host countries. I argue that journalists who work in London and who have joined the British National Union of Journalists (NUJ) see the NUJ as part of the British political scene and consider it to be a powerful potential tool in defending journalists' rights when reporting inside the Arab region.  相似文献   

Crime profiling for the Arabic language using computational linguistic techniques     

Meshrif Alruily  Aladdin Ayesh  Hussein Zedan 《Information processing & management》2014

Arabic is a widely spoken language but few mining tools have been developed to process Arabic text. This paper examines the crime domain in the Arabic language (unstructured text) using text mining techniques. The development and application of a Crime Profiling System (CPS) is presented. The system is able to extract meaningful information, in this case the type of crime, location and nationality, from Arabic language crime news reports. The system has two unique attributes; firstly, information extraction that depends on local grammar, and secondly, dictionaries that can be automatically generated. It is shown that the CPS improves the quality of the data through reduction where only meaningful information is retained. Moreover, the Self Organising Map (SOM) approach is adopted in order to perform the clustering of the crime reports, based on crime type. This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it, i.e. the data are cleansed by removing noise. The proposed system is validated through experiments using a corpus collated from different sources; it was not used during system development. Precision, recall and F-measure are used to evaluate the performance of the proposed information extraction approach. Also, comparisons are conducted with other systems. In order to evaluate the clustering performance, three parameters are used: data size, loading time and quantization error.  相似文献   

Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ reviews using morphological,syntactic and semantic features     

Mohammad Al-Smadi  Mahmoud Al-Ayyoub  Yaser Jararweh  Omar Qawasmeh 《Information processing & management》2019,56(2):308-319

This research presents an enhanced approach for Aspect-Based Sentiment Analysis (ABSA) of Hotels’ Arabic reviews using supervised machine learning. The proposed approach employs a state-of-the-art research of training a set of classifiers with morphological, syntactic, and semantic features to address the research tasks namely: (a) T1:Aspect Category Identification, (b) T2:Opinion Target Expression (OTE) Extraction, and (c) T3: Sentiment Polarity Identification. Employed classifiers include Naïve Bayes, Bayes Networks, Decision Tree, K-Nearest Neighbor (K-NN), and Support-Vector Machine (SVM).The approach was evaluated using a reference dataset based on Semantic Evaluation 2016 workshop (SemEval-2016: Task-5). Results show that the supervised learning approach outperforms related work evaluated using the same dataset. More precisely, evaluation results show that all classifiers in the proposed approach outperform the baseline approach, and the overall enhancement for the best performing classifier (SVM) is around 53% for T1, around 59% for T2, and around 19% in T3.  相似文献   

Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms     

Bassam Al-Salemi  Masri Ayob  Graham Kendall  Shahrul Azman Mohd Noah 《Information processing & management》2019,56(1):212-227

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.  相似文献   

PASCO数字化物理实验中测量结果的有效数字     

赖恒  邓伟 《福建师大福清分校学报》2009,(Z1)

PASCO数字化物理实验主要是使用电压、电流、位置和光强等传感器来测量多种物理量,这些传感器及其采集转换平台与传统仪器的使用一样也存在测量误差.本文主要采用对比研究的方法得出常用PASCO传感器及其采集转换平台测量结果的有效数字,为今后的数字化物理实验教学提供数据依据和参考.  相似文献   

毛泽东诗词翻译中模糊数字的处理     

魏艳  陈喜贝 《海外英语》2011,(11):188-189

模糊语言理论为数字模糊语义翻译开辟了新的途径。英汉数字在民间语言和文学作品中具有模糊语义,起到修辞和增加语势的作用。该文从英汉数字模糊语义出发,探讨毛泽东诗词英译中数字模糊语义的翻译方法。  相似文献   

希腊化科学衰落探源:以明清之交西学为参照     

毛丹 《科学与管理》2015,(3)

本文对比、分析了明清之交西学与希腊化晚期至元首制帝国初期希腊化科学的境遇,并通过阿拉伯科学的旁证表明,古代科学的昌盛需苛刻条件的汇集,衰落却只是区域政治演变的自然结果;由于这种演变在工业革命前的各文明区总是周期性地发生,因而,除非有特殊机缘,科学在某地区持续发展殊不可能。希腊化科学的衰落正是文明区政权更迭过程的正常反应。  相似文献   

Improving query precision using semantic expansion     

Ahmed Abdelali  Jim Cowie  Hamdy S. Soliman 《Information processing & management》2007

Query Expansion (QE) is one of the most important mechanisms in the information retrieval field. A typical short Internet query will go through a process of refinement to improve its retrieval power. Most of the existing QE techniques suffer from retrieval performance degradation due to imprecise choice of query’s additive terms in the QE process. In this paper, we introduce a novel automated QE mechanism. The new expansion process is guided by the semantics relations between the original query and the expanding words, in the context of the utilized corpus. Experimental results of our “controlled” query expansion, using the Arabic TREC-10 data, show a significant enhancement of recall and precision over current existing mechanisms in the field.  相似文献   

中文科技论文中顿号的使用问题     

田美娥  贺元旦 《出版科学》2008,16(4)

在科技编辑界,并列的阿拉伯数字之间和并列的外文字符之间的停顿用逗号而不用顿号,已成为一种约定俗成的规则。但这种用法可能引起3个问题：不符合中文标点符号使用规则,句子层次混乱甚至造成歧义,句中对应成分标点符号不对应。  相似文献   

学术期刊的数字用法及有关书写规则     

席庆义  缪莉莉 《合肥联合大学学报》2006,16(1):80-83

在出版物里数字使用的频率很高，为避免使用混乱，国家有关部门于1995年颁发了GB／T15835-1995标准，对数字用法作了统一规定，这对书报刊中数字使用的规范化起到十分重要的作用．但许多同志对此不熟悉。文稿中数字使用混乱的局面还较普遍．根据国家标准及有关文献对如何正确使用数字作一介绍，供作者参考，以期提高学报的标准化、规范化程度．  相似文献