عشقی الہاشمی(۱۹۰۹ء ۔۱۹۸۳ء)کا اصل نام جعفر علی اور عشقیؔ تخلص کرتے تھے۔ عشقیؔ سیالکوٹ کے سادات نقوی خاندان میں ہوئے۔ آپ عربی فارسی میں خدا داد قابلیت رکھتے تھے اور علومِ شرقیہ کے بہترین اساتذہ میں شمار ہوتے تھے۔ عشقیؔ نے شاعری میں علی طالب الہٰ آباد ی اور لسان الہند مرزا ہادی عزیز لکھنوی سے فیض حاصل کیا۔ سیالکوٹ میں عشقیؔ کے بہت زیادہ شاگرد تھے۔ جنھوں نے اُردو شاعری میں اعلیٰ مقام حاصل کیا۔ اصغر سودائیؔ اور تابؔ اسلم جیسے کاملِ فن شعرا عشقیؔ کے تلمذ میں رہے۔(۴۵۶)
آپ نے مجلہ در’’نجف‘‘ میں بحیثیت مدیر معاون کام کیا۔ ’’شبابِ اردو‘‘ ،اور’’نوروز‘‘ کی ادارت بھی سنبھالی ۔اور امر تسر کے ہفت روزہ ’’مجلہ آرٹ‘‘ کے مدیر بھی رہے۔ (۴۵۷) ’’سر شک بہار‘‘ ،’’مطلع الانوار‘‘ ،’’سوزو ساز‘‘ ،’’سہا و سمن‘‘ اور ’’غزلستان‘‘ عشقیؔ کے چار شعری مجموعے ہیں۔’’العروض ‘‘تصنیف میں فنِ شاعری پر تنقید اور تبصرے شامل ہیں۔(۴۵۸)
عشقیؔ روایتی شاعر ہیں ان کے ہاں کوئی جدت نظر نہیں آتی۔ عشقی ؔ کے اسلوب پر دبستان دہلی اورلکھنو کے اثرات بھی دیکھے جا سکتے ہیں ۔ اُن کی غزلیات چھوٹی اور لمبی بحروں میں ہیں ۔شاعری میں قافیہ اور ردیف پر بہت زور دیتے ہیں ۔ان کی اکثر غزلیات کی طویل ردیفیں ہیں ایسا لگتا ہے جیسے وہ شاعری پر قافیہ اور ردیف کو فوقیت دیتے ہیں ۔ مذکورہ بالا خامیوں کے باوجود عشقیؔ کے ہاں آفاقی موضوعاتِ شاعری بھی موجود ہیں۔ اخلاقیات،رجائیت،قومیت،حقیقت پسندی،اصلاح ،عشقِ مجازی اور عشقِ حقیقی عشقیؔ کی شاعری کے اہم موضوعات ہیں اس حوالے سے نمونہ کلام ملاحظہ ہو:
قوم پر جب زوال آتا ہے
/نوجوان بے لگام ہوتے ہیں
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Vivekananda was the Hindu revivalist of nineteenth century who preached the message of equality of all religions. In his lectures, he guided people to assimilation of religions and not their destruction. He emphasized on peace and harmony of religions that could only be attained through tolerance. This paper explores his claim of equality of religions from his writings regarding two religions - Christianity and Islam. Critical approach is utilized to identify the fact that either he is equating all religions or he is trying to submerge the non-Hindu religions in his philosophy of neo-Vedanta. Apparently, the tension between these two positions makes his theory of pluralism suspicious. In other words, his popular image of being a pioneer of religious pluralism needs to be reassesses by studying his writings and presentation of the beliefs, rituals, and practices of other religions, as well as, his practical interactions with the followers of different religions during his life. In this connection, it is important to explore how he perceives and depicts the personalities of Jesus Christ and Muhammad.
Biomedical knowledge is usually presented in the form of unstructured segments; making the extraction of such information a complex task. Although, manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually, because its data size is rising exponentially. Thus, there is a need for automatic tools and techniques for information extraction and knowledge discovery in biomedical text mining. Named entity recognition and relation extraction are focused areas of research in biomedical information extraction systems. Relation Extraction hinders the known relationship between Named Entities and in some way these are dependent on each other yet research also takes both these steps in an independent manner also. A lot of work has been done on biomedical named entity recognition focusing mostly on supervised and semi supervised solutions but very less attention work is done on unsupervised methods. Due to limited availability of annotated corpora the researchers now directed their efforts towards achievement of unsupervised named entity recognition systems. Named Entity Recognition from annotated corpora has been matured and there is very less margin for performance optimization. The challenge is still alive for the named entity recognition from unannotated corpora in all domains generally and for biological and biomedical domain specifically. Biomedical text exhibits relationships between different entities which are important for practitioners and researchers. Relation extraction is a significant area in biomedical knowledge, which has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction and identification focusing on two major areas: 1) rule based technique and 2) machine learning technique. In the last decade, focus has changed to hybrid approaches which have shown better results. This research presents an unsupervised named entity recognition framework along with a hybrid feature set for classification of relations between biomedical entities. Our Named Entity Recognition uses UMLS concepts and creates signatures that automate signature vectors. The vectorization of UMLS concepts ensures application of the framework in a generic way. Our framework differs with previous un-supervised methods in a way that we rely on UMLS for vector space creation instead of corpus statistics. The Relation Extraction approach uses bag of word feature, along with Natural Language Processing (NLP) to identify the noun and verb phrases and semantic features based on UMLS concepts. This hybrid feature set is a better representation of the relation extraction task. The main contribution in this hybrid features is the addition of semantic feature xi | P a g e set where verb phrases are ranked using Unified Medical Language System (UMLS), and a ranking algorithm is designed to get the most suitable concepts as features for the classifier. For Named Entity Recognition, we used Arizona Disease Corpus (AZDC) a gold standard corpus for this task. Our framework achieved accuracy of 72.56% which is competitive with supervised techniques on the same corpus. Our Relation Extraction approach has been validated on standard biomedical text corpus obtained from MEDLINE 2001, an accuracy of 96.19%, 97.45%, 96.49% and F-measure of 98.05%, 93.55%, 88.89% has been achieved for the cure, prevent and side effect relations respectively.