Automatic Kurdish Text Classification Using KDC 4007 Dataset

Rashid, Tarik A.; Mustafa, Arazo M.; Saeed, Ari M.

doi:10.1007/978-3-319-59463-7_19

Cited by 18 publications

(17 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Term Frequency-Inverse Document Frequency (TF-IDF) is the product of two statistics TF and IDF [10], [30]; it used to determine the frequency of each word in a hadith text according to equation 3, TF-IDF value increases proportionally to the number of times a word appears in the text. TF i,j is to calculate the term frequency using equation 1…”

Section: ) Tf-idfmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

2019

View full text Add to dashboard Cite

Given the importance of the Prophet's Hadith for Muslims all over the world, where it is the second source of Islam after the Qur'an and the fundamental resource of legislation in the Islam community. This study is focused on the Classification of hadith automatically into different categories according to its content, based on Hadith text. The objective of this study is to build a classifier model can classify and differentiate hadith categories, to predict its topic like prayer, fasting, and zakat; using data mining and machine learning techniques. In this study, many supervised learning algorithms plus combination methods such as the stacking algorithm was used to improve classification accuracy. The best three classifiers were evaluated mainly: the Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB), which achieved higher accuracy reached up to 0.965%, 0.956, and 0.951% respectively. Also, Binary (Boolean algebra) and TF-IDF methods as term weighting was applied to determine the frequency of each word in the hadith text, and identify the most significant features in training dataset using Information Gain (IG), and Chi-square (CHI). The experimental results showed that retrain these classifiers after applying IG and CHI as features selection; gave better accuracy compared to the previous results. Additional to, the best classifier gave high accuracy was DT, it has achieved higher accuracy in most test cases whether in the Boolean algebra or TF-IDF because it can deal with missing values and identifying the most essential features from the training dataset, known as features engineering.

show abstract

Section: ) Tf-idfmentioning

confidence: 99%

“…Also, the precision and the recall could be computed by using Equations 9 and 10, respectively. Finally, the F-measure is used to calculate the performance of classifiers according to Equation 11 [10], [30].…”

Section: F Classifier Model Evaluatingmentioning

confidence: 99%

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

2019

View full text Add to dashboard Cite

show abstract

“…Machine translation is one of the important applications of WSD and is discussed [22], [23]. A survey of text classification of Kurdish language is beautifully presented [24]- [27] where they applied stemmer algorithm to find the stem to perform classification. WSD network approach, sentiment analysis and survey is explored [28]- [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Wiki sense bag creation using multilingual word sense disambiguation

Patankar

Phadke

Devane

2022

IJ-AI

View full text Add to dashboard Cite

Performance of word sense disambiguation (WSD) is one of the challenging tasks in the area of natural language processing (NLP). Generation of sense annotated corpus for multilingual word sense disambiguation is out of reach for most languages even if resources are available. In this paper we propose an unsupervised method using word and sense embedding or improving the performance of these systems using untagged. Corpora and create two bags namely ontological bag and wiki sense bag to generate the senses with highest similarity. Wiki sense bag provides external knowledge to the system required to boost the disambiguation accuracy. We explore Word2Vec model to generate the sense bag and observe significant performance gain for our dataset.

show abstract

“…Central Kurdish ( Sorani ) is one of two main dialects of the Kurdish language, it is generally thought that Sorani is spoken by about 9 to 10 million people in Iraq and Iran [ 1 , 2 ]. It is mainly written using a modified Arabic/Persian alphabet containing 34 characters, including characters that have been replaced in recent years like (ك) that's no longer been used by the Kurdish language and replaced with (ک).…”

Section: Data Descriptionmentioning

confidence: 99%

An extensive dataset of handwritten central Kurdish isolated characters

et al. 2021

Self Cite

View full text Add to dashboard Cite

To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil.

show abstract

Automatic Kurdish Text Classification Using KDC 4007 Dataset

Cited by 18 publications

References 12 publications

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

Wiki sense bag creation using multilingual word sense disambiguation

An extensive dataset of handwritten central Kurdish isolated characters

Contact Info

Product

Resources

About