KLPT – Kurdish Language Processing Toolkit

Ahmadi, Sina

doi:10.18653/v1/2020.nlposs-1.11

Cited by 17 publications

(22 citation statements)

References 46 publications

(16 reference statements)

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“…As the Kurdish language is one of the less-resourced Indo-European languages and written using different scripts, it still lacks digital text resources to enable NLP applications [7]. The Kurdish scripts lack standardized orthographies and create differences in writing words, particularly compound forms [8].…”

Section: Natural Language Processingmentioning

confidence: 99%

“…The tokenization is the fundamental step in the NLP pipeline, which is required for the advanced steps such as part-of-speech tagging, syntactic analysis, and machine translation [9]. The Kurdish language has prefixes and suffixes attaching to a lemma, therefore it has a complex structure in terms of morphology, especially in the Sorani dialect.…”

Section: Kurdish Language Characteristicsmentioning

confidence: 99%

“…) xwardinewe) (drink) are two distinct lexemes in Kurdish, they both have one lemma and that is xwardin [8]. There are two main types of orthography that are used to write the text in the Kurdish language, they are Arabic-based and Latin-based:…”

Section: Kurdish Language Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

The Kurdish Language Corpus: State of the Art

Azzat

Jacksi

Ali

2023

SJUOZ

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The notable growth of the digital communities and different online news streams led to the growing availability of online natural language content. However not all natural languages have the enough attention of being made readable and comprehendible to machines. Among these less resourced and paid attention languages is the Kurdish language. Creating the machine-readable text is the first step toward applications of text mining and semantic web, such as translation, information retrieval and recommendation systems. With the de facto challenges in the Kurdish language, such as the scarcity of linguistic sources and not having unified orthography rules, this language has a lack of the language processing tools. However, to overcome the mentioned challenges and enable intelligent applications the well organized and annotated Kurdish text corpora is needed. This review paper investigates the available textual corpora in the Kurdish language and its dialects and then determined challenges are discussed, open problems are listed and future directions suggested.

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Section: Natural Language Processingmentioning

confidence: 99%

Section: Kurdish Language Characteristicsmentioning

confidence: 99%

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The Kurdish Language Corpus: State of the Art

Azzat

Jacksi

Ali

2023

SJUOZ

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“…Thus, pre-processing would be challenging since the language has progressed in NLP. Luckily, we could use the python KLPT toolkit developed by Ahmadi (2020). The libraries on KLPT helped us with normalization, standardization, and tokenization.…”

Section: Introductionmentioning

confidence: 99%

Using Multilingual Bidirectional Encoder Representations from Transformers on Medical Corpus for Kurdish Text Classification

Badawi

2023

ARO

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Technology has dominated a huge part of human life. Furthermore, technology users use language continuously to express feelings and sentiments about things. The science behind identifying human attitudes toward a particular product, service,or topic is one of the most active fields of research, and it is called sentiment analysis. While the English language is making real progress in sentiment analysis daily, other less-resourced languages, such as Kurdish, still suffer from fundamental issues and challenges in Natural Language Processing (NLP). This paper experimentswith the recently published medical corpus using the classical machine learning method and the latest deep learning tool in NLP and Bidirectional Encoder Representations from Transformers (BERT). We evaluated the findings of both machine learning and deep learning. The outcome indicates that BERT outperforms all the machine learning classifiers by scoring (92%) in accuracy, which is by two points higher than machine learning classifiers.

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“…For the Central Kurdish language, a number of works have been done on NLP. There have been a number of studies on spell-checking and stemming [12] [13] [14] [15] [16], and [17] produce Kurdish Language Processing Toolkit (KLPT) their toolkit is composed of basic elements like text pre-processing, tokenization, stemming, transliteration and lemmatization. There is an n-gram-based document classifier [18], also with respect to their efforts, there are some small efforts to create a lexicon and corpus for Kurdish [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Central Kurdish Sentiment Analysis Using Deep Learning

Awlla¹,

Veisi²

2022

JUAPS

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Sentiment Analysis (SA) as a type of opinion mining and as a more general topic than polarity detection, is widely used for analyzing user's reviews or comments of online expressions, which is implemented using various techniques among which the Artificial Neural Network (ANN) is the most popular one. This paper addresses the development of an SA system for the Central Kurdish language (CKB) using deep learning. Increasing the efficiency and strengthening of the SA system relies on a robust language model. In addition, for creating and training a robust language model, collecting a large amount of text corpus is required and we have created a corpus of size 300 million tokens for CKB. Also, to train the SA model, we collected 14,881 comments on Facebook, then they are labeled manually. The combination of Word2Vec for the language model and Long Short-Term Memory (LSTM) for the classifier are used to create an SA model on the CKB SA dataset. These deep learning-based techniques are the most well-known methods in this field which have received high performance in SA for various languages. The performance of the proposed method for 3 classes SA is %71.35 accuracy. This result is superior to the best-reported result for CKB..

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KLPT – Kurdish Language Processing Toolkit

Cited by 17 publications

References 46 publications

The Kurdish Language Corpus: State of the Art

The Kurdish Language Corpus: State of the Art

Using Multilingual Bidirectional Encoder Representations from Transformers on Medical Corpus for Kurdish Text Classification

Central Kurdish Sentiment Analysis Using Deep Learning

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