پروفیسر سید عبدالرحیم
پروفیسر سید عبدالرحیم کچھ دنوں سے کینسر کے موذی مرض میں مبتلا ہوگئے تھے بالآخر ۱۶؍ فروری ۲۰۰۵ء کو ناگ پور میں اپنے مالک حقیقی سے جاملے، اناﷲ وانا الیہ راجعون۔
مرحوم کا آبائی وطن ایلچ پور تھا، لیکن وہ اپنے نانہال ’’بھی گاؤں‘‘ ضلع امراوتی میں ۱۴؍ اپریل ۱۹۳۲ء کو پیدا ہوئے، ایلچ پور میں ابتدائی تعلیم حاصل کی اور وہیں کے رحمانیہ اردو ہائی اسکول سے میٹرک کا امتحان پاس کیا، ۱۹۵۴ء میں ناگ پور یونیورسٹی سے بی اے اور ۱۹۵۶ء میں فارسی میں ایم اے کیا، ۱۹۴۶ء میں اردو میں ایم اے کیا اور کلکتہ یونیورسٹی سے عربی میں ایم اے کیا، ۱۹۷۷ء میں ’’ارادت خاں‘‘ پر تحقیقی مقالہ لکھ کر ناگ پور یونیورسٹی سے پی ایچ ڈی کی ڈگری لی۔
کچھ عرصے تک انجمن ہائی اسکول کھام گاؤں میں درس کی خدمت انجام دی پھر محکمہ آثار قدیمہ سے وابستہ ہوئے جس کے ڈایرکٹر ڈاکٹر ضیاء الدین احمد ڈیسائی مرحوم تھے، ان کی رہنمائی میں ان کو بھی تحقیق اور تلاش و جستجو کا چسکا لگا اور کتابت و مخطوطات شناسی سے دلچسپی پیدا ہوئی۔
عبدالرحیم صاحب ۱۹۶۸ء میں وسنت راؤ نایک انسٹی ٹیوٹ آف آرٹس اینڈ سوشل سائنسز میں اردو فارسی اور عربی کے لیکچرر مقرر ہوئے اور ۱۹۷۷ء میں پروفیسر کے عہدے پر فایز ہوئے، ۱۹۸۸ء میں کالج کوانسٹی ٹیوٹ کا درجہ دیا گیا تو یہ ڈایرکٹر مقرر کیے گئے اور ۱۹۹۲ء میں اسی عہدے سے سبک دوش ہوئے۔
ڈاکٹر صاحب مہاراشٹر اور گجرات کی مختلف علمی، تعلیمی، ادبی اور ثقافتی سرگرمیوں سے وابستہ تھے اور کئی اداروں اور اکیڈمیوں کے ممبر اور بعض کے چیرمین بھی رہے، ان کی نگرانی میں متعدد لوگوں نے علمی و تحقیقی کام انجام دیے اور پی ایچ ڈی کی ڈگری بھی حاصل کی، ان کا ایک بڑا...
The Holy Quran is one of the most vibrant fields of knowledge in Islamic Sharī’ah because it is the primary source of Islamic Law and teachings. It has many sub-fields and branches of knowledge. One of the most significant fields of the Holy Quran’s knowledge is ‘Ilm al-Qira’at (Science of Qurānic Variants). It means the recitation of Quran in various styles, accents, methodology and approaches. According to deeper Islamic perception of Knowledge the Holy Quran was reveled in seven Ahruf (accents, meanings, phrases and styles). This Knowledge ultimately deals with these styles and methodologies. The wise and faithful companions of the Holy Prophet (pbuh) get more and more expertise under the divine leadership of the Kind Prophet (pbuh). After it the nearest era of the Holy Prophet (pbuh) is the era of Tābī’n (Successors of the Companions of the Holy Prophet). They worked hard and get more height and boom in different domains of knowledge of Islamic Sharī’ah. They served every field of Islamic sciences and knowledge but emphases on one of them and became (leader) Imam of it.
In this research the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by using the Lexicon-based approach. In the lexicon, apart from the traditional approach that considers adjectives only, nouns and verbs are also included. An efficient Urdu Sentiment Analyzer is developed that applies rules and makes use of this new lexicon to perform Urdu Sentiment Analysis by classifying sentences as positive, negative or neutral. Negations, intensifiers and context-depentent words are effectively handled for enhancing accuracy of Urdu Sentiment Analyzer. Specific rules for handling negations, intensifiers and context-dependent words are incorporated in Urdu Sentiment Analyzer. For testing the Lexicon-based approach, a corpus of 6025 sentences from 151 blogs belonging to 14 different genres is collected and the sentences are annotated by three human annotators to classify each sentence as positive, negative and neutral. Evaluating this Urdu Sentiment Analyzer, by using sentences from the corpus, yields the most promising results so far in Urdu language (up to the knowledge of the author) with 89.03% accuracy, 0.86 precision, 0.90 recall and 0.88 f-measure. The comparison with the previous works in Urdu Sentiment Analysis shows that the combination of this Urdu Sentiment Lexicon and Urdu Sentiment Analyzer is much more effective than the previous such combinations. The main reason for increased efficiency is the development of wide coverage lexicon and effective handling of negations, intensifiers and context-dependent words by the Urdu Sentiment Analyzer. Although high accuracy is achieved by Lexicon-based approach in multiple domains for Urdu Sentiment Analysis, which is the main objective of this research, but for comparison, Supervised Machine Learning approach is also used. Three well known classifiers that are Support Vector Machine, Decision Tree and K Nearest Neighbor are tested; their outputs are compared and their results are ultimately improved in several iterations. It is further concluded that K Nearest Neighbor is performing better than Support Vector Machine and Decision Tree. For verification of this result, three evaluation measures i.e. McNemar’s Test, Kappa Statistic and Root Mean Squared Error are used. The result from all these three evaluation measures confirmed that K Nearest Neighbor is performing much better than the other two classifiers and achieved 67.02% accuracy, 0.68, 0.67 and 0.67 precision, recall and f-measure respectively. The results from both the approaches are compared. On the basis of experiments performed in this research, it is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach, when Urdu Sentiment Analysis is performed in multiple domains in terms of accuracy, precision, recall and f-measure, economy of time and effort.