A Rule-Based Kurdish Text Transliteration System

Ahmadi, Sina

doi:10.1145/3278623

Cited by 24 publications

(17 citation statements)

References 12 publications

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“…Several scholars have addressed transliterators for Kurdish scripts (Esmaili et al, 2014;Hassani, 2018;Ahmadi, 2019). Those studies mostly focused on transliteration from Persian-Arabic into Latin script.…”

Section: Related Workmentioning

confidence: 99%

“…Those studies mostly focused on transliteration from Persian-Arabic into Latin script. Particularly, Ahmadi (2019) addressed several important issues in transliterating Kurdish texts in Persian-Arabic into Latin script and proposed and implemented appropriate resolutions for them.…”

Section: Related Workmentioning

confidence: 99%

“…However, not all Kurdish readers are familiar with both mentioned scripts that could be resolved by automatic transliterators. So far, the developed tools mostly transliterate Persian-Arabic scripts into Latin (Hassani, 2018;Ahmadi, 2019). We present a transliterator to transliterate Kurdish texts in Latin into Persian-Arabic script.…”

Section: Introductionmentioning

confidence: 99%

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Transliterating Kurdish texts in Latin into Persian-Arabic script

Hassani¹

2021

Preprint

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Kurdish is written in different scripts. The two most popular scripts are Latin and Persian-Arabic. However, not all Kurdish readers are familiar with both mentioned scripts that could be resolved by automatic transliterators. So far, the developed tools mostly transliterate Persian-Arabic scripts into Latin. We present a transliterator to transliterate Kurdish texts in Latin into Persian-Arabic script. We also discuss the issues that should be considered in the transliteration process. The tool is a part of Kurdish BLARK, and it is publicly available for non-commercial use 1 .

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Transliterating Kurdish texts in Latin into Persian-Arabic script

Hassani¹

2021

Preprint

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“…Ahmadi et al [1] have essentially centered in giving better extraction of data from images by improving the nature of the images and by utilizing robotized image handling strategies. To improve the image quality one of the most straightforward and powerful systems is Histogram Equalization (HE).…”

Section: Literature Surveymentioning

confidence: 99%

A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network

Maddumala¹,

Arunkumar²

2020

ISI

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This paper intends to present main technique for feature extraction on multimeda getting well versed and a challenging task to handle big data. Analyzing and feature extracting valuable data from high dimensional dataset challenges the bounds of measurable methods and strategies. Conventional techniques in general have less performance while managing high dimensional datasets. Lower test size has consistently been an issue in measurable tests, which get bothered in high dimensional information due to more equivalent or higher component size than the quantity of tests. The intensity of any measurable test is legitimately relative to its capacity to lesser an invalid theory, and test size is a significant central factor in producing probabilities of errors for making substantial ends. Thus one of the effective methods for taking care of high dimensional datasets is by lessening its measurement through feature selection and extraction with the goal that substantial accurate data can be practically performed. Clustering is the act of finding hidden or comparable data in information. It is one of the most widely recognized techniques for realizing useful features where a weight is given to each feature without predefining the various classes. In any feature selection and extraction procedures, the three main considerations of concern are measurable exactness, model interpretability and computational multifaceted nature. For any classification model, it is important to ensure that the productivity of any of these three components isn't undermined. In this manuscript, a Weight Based Feature Extraction Model on Multifaceted Multimedia Big Data (WbFEM-MMB) is proposed which extracts useful features from videos. The feature extraction strategies utilize features from the discrete cosine methods and the features are extracted using a pre-prepared Convolutional Neural Network (CNN). The proposed method is compared with traditional methods and the results show that the proposed method exhibits better performance and accuracy in extracting features from multifaceted multimedia data.

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“…But IE is easier for rich morphological languages like Russian and English. Several solutions for various languages such as Urdu [16], Malayalam [17], and Kurdish [18] are proposed in literature.…”

Section: A Information Extraction From Text Datamentioning

confidence: 99%

Information Extraction from Multifaceted Unstructured Big Data

2019

IJRTE

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In the era of digital globalization, huge volume and variety of data are being produced at a very high rate. Every day, the world is producing around 2.5 quintillion bytes of data. According to IDC, by 2020, over 40 zettabytes of data will be generated and reproduced. Digital data have become a deluge, overwhelming in every field of information technology (IT), business, science and engineering. These fields are shifting to smart and advanced technologies such as smart manufacturing industries, data-aware medical sciences, and other smart applications. These applications are facilitating the industries in context of data-driven decision making, big data storage, and complex analysis of large data sets. Also, these applications are contributing to generate big data deluge where a variety of data necessitate the industries to use advanced IT approaches. 95% of the digital universe is unstructured data. It is rich data as it contains information that can play a vital role to improve big data analytics. The heterogeneity, complexity, lack of structured information, poor quality and scalability of unstructured data generates difficulties in adapting traditional information extraction techniques. Information extraction can play a vital role in transformation of unstructured data into useful information. A multistep pipeline with data preprocessing steps, extraction methods and representation are utmost requirement to improve the unstructured data analytics. In this regard, this paper presents a short review of information extraction process w.r.t. input data type, extraction methods with their corresponding techniques, and representation of extracted information. The issues with unstructured data and the challenges to information extraction from multifaceted unstructured big data as well as the future research directions have also been discussed

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A Rule-Based Kurdish Text Transliteration System

Cited by 24 publications

References 12 publications

Transliterating Kurdish texts in Latin into Persian-Arabic script

Transliterating Kurdish texts in Latin into Persian-Arabic script

A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network

Information Extraction from Multifaceted Unstructured Big Data

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