Zakat on Non-Agricultural Land Plots: Public Awareness and Fiscal Reforms

Zakat is although a compulsory obligation in Islam but is not paid compulsorily by the people. There are two main reasons; one is the ignorance about many things on which zakat is due, for example non-agricultural land and commercial plots etc. andsecond, ignorance about the mechanism through which it is paid. Thefirst aspect is related to public awareness and second aspect is related to the fiscal policy being enforced by the government collecting property and wealth taxes thus putting heavyfinancial pressure on the owners of real estate so as to compel them to avoid zakat andpay taxes. The paper explores the level of awareness among the general public about the payment ofzakat on non-agricultural land. Itfurther addresses the question that how the existing revenue structure need to be changed. For thefirst aspect, primary data with n=33 has been collected from the capital city of the province Khyber Pakhtunkhwa. For the second aspect the revenue structure of the government is reviewed tofind out the number of taxes on the possession, sale and purchase of commercial or nonagricultural land plots. Since taxes are in no way a substitute of zakat so it is further explored that if the existing taxes are replaced and zakat system would be properly enforced then how much is the net return. In order to estimate this net return, an interview has been conducted from the property dealers and also the figures are taken from government accounts to calculate a sample for the study area that is then generalized for the government returns at large. The results of the study show a high level ofignorance about zakat obligation on plots in the study area. The verdicts of the Islamic scholars also create difference of opinion, however, majority have approved the case in favor of payment. Since there is ignorance and people have to pay taxes so they avoid zakat and just pay taxes. A mechanism is needed for fiscal reforms to ensure the payment of zakat and to stop its substitution against taxes on land plots.

Generic Urdu Nlp Framework for Urdu Text Analysis: Hybridization of Heuristics and Machine Learning Techniques

The internet was initially designed to present information to users in English. However, with the passage of time and the development of standard web technologies such as browsers, programming languages, libraries, frameworks, databases, front and back-ends, protocols, APIs, and data formats, the internet became a multilingual source of information. In the last few years, the natural language processing (NLP) research community has observed a rapid growth in online multilingual contents. Thus, the NLP community maims to explore monolingual and cross-lingual information retrieval (IR) tasks. Digital online content in Urdu is also currently increasing at a rapid pace. Urdu, the national language of Pakistan and the most widely spoken and understandable language of Indian sub-continent, is considered a low-resources language (Mukund, Srihari, & Peterson, 2010). Part of speech (POS) tagging and named entity recognition (NER) are considered the most basic NLP tasks. Investigation of these two tasks in Urdu is very hard. POS tagging, the assignment of syntactic categories for words in running text is significant to natural language processing as a preliminary task in applications such as speech processing, information extraction, and others. Named entity recognition (NER) corresponds to the identification and classification of all proper nouns in texts, and predefined categories, such as persons, locations, organizations, expressions of times, quantities and monetary values, etc. it is considered as a sub-task and/or sub-problem in information extraction (IE) and machine translation. NER is one of the hardest task in Urdu language processing. Previously majority Urdu NER systems are based on machine learning (ML) models. However, the ML model needs sufficiently large annotated corpora for better performance(Das, Ganguly, & Garain, 2017). Urdu is termed as a scared resource language in which sufficiently large annotated corpus for ML models’ evaluation is not available. Therefore, the adoption of semi-supervised approach which is largely dependent on usage of the huge amount of unlabeled data is a feasible solution. In this thesis, we propose a generic Urdu NLP framework for Urdu text analysis based on machine learning (ML) and deep learning approaches. Initially, we addressed POS challenges by developing a novel tagging approach using the linear-chain conditional random fields (CRF). We employed a strong, stable, balanced language-independent and language dependent feature set for Urdu POS task and used the method of context words window. Our approach was evaluated against a support vector machine (SVM) technique for Urdu POS - considered Abstract WAHAB KHAN Reg: No. 72-FBAS/PHDCS/S12 vi as the state of the art - on two benchmark datasets. The results show our CRF approach to improving upon the F-measure of prior attempts by 8.3 to 8.5%. Secondly, we adopted deep recurrent neural network (DRNN) learning algorithms with various model structures and word embedding as a feature for the task of Urdu named entity recognition and classification. These DRNN models include long short-term memory (LSTM) forward recurrent neural network (RNN), LSTM bi-directional RNN, backpropagation through time (BPTT) forward RNN and BPTT bi-directional RNN. We consider language-dependent features such as part of speech (POS) tags as well as language independent features such as N-grams. Our results show that the proposed DRNN-based approach outperforms existing work that employ CRF based approaches. Our work is the first to use DRNN architecture and word embedding as a feature for Urdu NER task and improves upon prior attempts by 9.5% in the case of maximum margin.