In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation and others. It is a task that first identifies appropriate syntactic categories for each word in running text and secondly assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work [1]. Previously we presented conditional random field (CRF) based POS tagger with both language dependent and independent feature set. However, in the current study we offer a) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and b) to highlight diverse challenges which causes Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN) and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward Long Short-Term Memory (LSTM)-RNN and LSTM-RNN with CRF output.

Research efforts in the field of sentiment analysis have exponentially increased in the last few years due to its applicability in areas such as online product purchasing, marketing, and reputation management. Social media and online shopping sites have become a rich source of user-generated data. Manufacturing, sales, and marketing organizations are progressively turning their eyes to this source to get worldwide feedback on their activities and products. Millions of sentences in Urdu and Roman Urdu are posted daily on social sites, such as Facebook, Instagram, Snapchat, and Twitter. Disregarding people’s opinions in Urdu and Roman Urdu and considering only resource-rich English language leads to the vital loss of this vast amount of data. Our research focused on collecting research papers related to Urdu and Roman Urdu language and analyzing them in terms of preprocessing, feature extraction, and classification techniques. This paper contains a comprehensive study of research conducted on Roman Urdu and Urdu text for a product review. This study is divided into categories, such as collection of relevant corpora, data preprocessing, feature extraction, classification platforms and approaches, limitations, and future work. The comparison was made based on evaluating different research factors, such as corpus, lexicon, and opinions. Each reviewed paper was evaluated according to some provided benchmarks and categorized accordingly. Based on results obtained and the comparisons made, we suggested some helpful steps in a future study.

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation...

Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach.

The ability to control the spread of COVID-19 continues to be hampered by a lack of rapid, scalable, and easily deployable diagnostic solutions. We developed a diagnostic method based on CRISPR (clustered regularly interspaced short palindromic repeats) that can deliver sensitive, specific, and high-throughput detection of Sudden Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). The assay utilizes SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) for the qualitative detection of SARS-CoV-2 RNA and may be performed directly on a swab or saliva sample without nucleic acid extraction. The assay uses a 384-well format and provides results in <1 hour. Assay performance was evaluated with 105 (55 negative, 50 positive) remnant SARS-CoV-2 specimens previously tested using Food and Drug Administration emergency use authorized assays and retested with a modified version of the Centers for Disease Control and Prevention (CDC) quantitative PCR with reverse transcription (RT–qPCR) assay. When combined with magnetic bead-based extraction, the high-throughput SHERLOCK SARS-CoV-2 assay was 100% concordant (n = 60) with the CDC RT–qPCR. When used with direct sample addition the high-throughput assay was also 100% concordant with the CDC RT–qPCR direct method (n = 45). With direct saliva sample addition, the negative and positive percentage agreements were 100% (15/15, 95% CI: 81.8–100%) and 88% (15/17, 95% CI: 63.6–98.5%), respectively, compared with results from a collaborating clinical laboratory. This high-throughput assay identifies SARS-CoV-2 from patient samples with or without nucleic acid extraction with high concordance to RT–qPCR methods. This test enables high complexity laboratories to rapidly increase their testing capacities with simple equipment.