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تاریک دور

تاریک دور

جب پاکستان میں ایک تاریک دور کا آغاز ہوا ۔پھانسی کوڑے طویل المعیاد سزائیں سیاسی کارکنوںکا مقدر بنیں ۔پاکستان کے سیاسی ،سماجی کلچر کو یکسر تبدیل کر دیا گیا ۔کلاشنکوف کلچر اور سعودی برانڈ اسلام کو درآمد کیا گیا ۔روس افغانستان جنگ میں دلالی جہادی کلچر کے فروغ نے پاکستان کو بارود کے ڈھیر میں بدل دیا ۔جس کی آگ میںہم آج تک سلگ رہے ہیں ۔

 

Research on Learning Strategies in Arabic Language Education

The learning of Arabic language like any other foreign language contains four main aspects; reading, writing, speaking and understanding while listening.[i] This learning process can be enhanced if the most appropriate Learning Strategy is used. In this paper the most appropriate Learning Strategy of Arabic Language is suggested. The course outlines for Arabic language are thoroughly studied and several professors and experts of Arabic Language from Pakistan, Egypt, Saudi Arabia and Sudan are interviewed. The author, who himself has vast experience in teaching Arabic language, also had the opportunity to sit in the Arabic language classes to observe various strategies and methodologies adopted by different professors while teaching Arabic. In this paper the time spent on teaching Arabic to the students is also discussed. The appropriate size of the class room i.e, the number of students in Arabic language class also matters in improving the quality of Arabic among the students. The matter of teaching Arabic in Arabic only or in the native language of the students will also be touched in here. As the time has changed and the world is moving ahead on a fast pace, it seems necessary to apply the “Direct Method” while teaching Arabic or any foreign language.[ii] This paper will shed light on what is meant by “Direct Method”. The idea of making the student sit and memorize the dry rules of grammar has become obsolete. The idea of telling the student what part of the phrase is subject or predicate, or what is object and what is a noun or verb, may come later. The idea of memorizing the bulk of new vocabulary in the beginning can also be postponed. Hence a paradigm shift is needed here while talking about the Methodology of Teaching Arabic Language, under the heading of “Direct Method”.   [i]     Muhammad Abdul Khaliq, Professor of Arabic and co-author of 'al-Arabia baina Yadaik'. The author of this research paper had a personal interview with him on 21.03.2014, in the Institute of Arabic Language, King Saud University, Riyadh, Saudi Arabia. [ii]    This method is adopted roughly by some great scholars of Arabic language like Dr. V. Abdur Rahim who taught Arabic language for decades in the Islamic University of Madina, Kingdom of Saudi Arabia. The author was fortunate to meet with him many times and get benefitted from his experience. See for details: Abdurrahim, V. (1999), Arabic Course for English-Speaking Students, Leicester: UK Islamic Academy. See also: Abdullah, F. Ibrahim. (1999), Iqra Arabic Reader. Chicago: Iqra International Educational Foundation. Moreover see: Fawzan, Abdurrahman and others. (2004), Al-Arabia Baina Yadaik, Riyadh: Ministry of Education.

Sentiment Analysis for Sindhi Text

Sentiment analysis is basically opinion mining or emotion analysis. Many people express their views and sentiments through verbal, non-verbal and written forms to show their opinions and emotions on products, personalities, tourist places, educational institutions, hospitals, historical places, government, restaurants etc. A number of organizations are planning and concentrating on views and opinions of people to get some useful information. The social media, public and private sector organizations websites, web pages, blogs and online surveys are the important sources for getting opinions and reviews of people, thus, word wide web is best source of generating such types of data. Sentiment analysis, review analysis, emotion detection and opinion mining are procedures of analysing the unstructured or structured data for the purpose of evaluation of sentiments and opinions. Sentiments show the scale or level of confidence for positive opinion, negative opinion or neutral opinion or sentiments. Today, sentiments and opinions or reviews evaluation are one of the significant attentions of Natural Languages Processing generally called NLP. Majority of computational linguistics and sentiment analysis etc. software applications are existing for English and some other languages, nonetheless, numerous languages are there which cannot meet the level and category of these types of languages. Though, research studies and tools development processes are in growth for the languages, which are not resourced languages yet. The Sindhi language is an Asian language, which may be called the morphologically rich language, nevertheless, it faces several complexities since evaluating and analysing the online or offline text. Though, lots of data are available online or offline in different forms but yet no appropriate research study or work has discovered in the field of NLP as well as on sentiment analysis for Sindhi language text particularly. The deficiency of development work and research studies as well as technical resources for Sindhi language make the current research work or study interesting and challenging. Viewing and assessing this challenge, we have taken this task to work more to address the problems of Sindhi language data. Therefore, we have focused the construction of text corpus, data set, sentiment analysis system, word tokenization, part of speech tagging as well as subjective lexicon assessment for Sindhi language text. Supporting tools such as Sindhi POS tagger helps in identifying sentiments from Sindhi text corpus. This study has developed the NLP resources including sentiment analysis resources for Sindhi language text. Separate text corpus and linguistic data sets are developed and analysed by machine learning and deep learning models. Machine learning models are trained with small sentiment-based Sindhi training data and large sentiment-based Sindhi training data. The results confirm the proper performance and execution of supervised machine learning models in form of extraction of appropriate sentiments. The sentiment analysis for Sindhi text is done on document-level sentiment analysis, product level and aspect level sentiment analysis. The leaning model is designed and developed for the purpose of sentiment evaluation and analysis for Sindhi language text. Neural network based LSTM model is used with multiple layers to evaluate and validate the sentiment based Sindhi language text and products feature based data set. Results of models confirm the significance of methodology by showing good sentiment analysis and opinion analysis on Sindhi language text. Research study contributes the Sindhi language plain text corpus, linguistics dataset, aspect-based sentiment analysis dataset to the fields of natural languages processing as well as computational linguistics. Sentiment analysis system, which is developed for the Sindhi text is significant and state-ofthe art work. The work places the Sindhi language for international research to explore the grammatical and morphological complexities, perform the information retrieving, language modelling, semantic and sentiment analysis, universal dependencies and unsupervised modelling for text analysis etc.
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