arXiv subjects

Hossein Hassani

Publications and source records attributed to Hossein Hassani.

31 records · Page 2Linked to original sources

arXiv2022

Part of Speech Tagging (POST) of a Low-resource Language using another Language (Developing a POS-Tagged Lexicon for Kurdish (Sorani) using a Tagged Persian (Farsi) Corpus)

Tagged corpora play a crucial role in a wide range of Natural Language Processing. The Part of Speech Tagging (POST) is essential in developing tagged corpora. It is time-and-effort-consuming and costly, and therefore, it could be more affordable if it is automated. The Kurdish language currently lacks publicly available tagged corpora of proper sizes. Tagging the publicly available Kurdish corpora can leverage the capability of those resources to a higher level than what raw or segmented corpora can provide. Developing POS-tagged lexicons can assist the mentioned task. We use a tagged corpus (Bijankhan corpus) in Persian (Farsi) as a close language to Kurdish to develop a POS-tagged lexicon. This paper presents the approach of leveraging the resource of a close language to Kurdish to enrich its resources. A partial dataset of the results is publicly available for non-commercial use under CC BY-NC-SA 4.0 license at https://kurdishblark.github.io/. We plan to make the whole tagged corpus available after further investigation on the outcome. The dataset can help in developing POS-tagged lexicons for other Kurdish dialects and automated Kurdish corpora tagging.

cs.CL↗

arXiv2021

Comparing the Accuracy of Deep Neural Networks (DNN) and Convolutional Neural Network (CNN) in Music Genre Recognition (MGR): Experiments on Kurdish Music

Musicologists use various labels to classify similar music styles under a shared title. But, non-specialists may categorize music differently. That could be through finding patterns in harmony, instruments, and form of the music. People usually identify a music genre solely by listening, but now computers and Artificial Intelligence (AI) can automate this process. The work on applying AI in the classification of types of music has been growing recently, but there is no evidence of such research on the Kurdish music genres. In this research, we developed a dataset that contains 880 samples from eight different Kurdish music genres. We evaluated two machine learning approaches, a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN), to recognize the genres. The results showed that the CNN model outperformed the DNN by achieving 92% versus 90% accuracy.

cs.SD↗

arXiv2021

Can Linguistic Distance help Language Classification? Assessing Hawrami-Zaza and Kurmanji-Sorani

To consider Hawrami and Zaza (Zazaki) standalone languages or dialects of a language have been discussed and debated for a while among linguists active in studying Iranian languages. The question of whether those languages/dialects belong to the Kurdish language or if they are independent descendants of Iranian languages was answered by MacKenzie (1961). However, a majority of people who speak the dialects are against that answer. Their disapproval mainly seems to be based on the sociological, cultural, and historical relationship among the speakers of the dialects. While the case of Hawrami and Zaza has remained unexplored and under-examined, an almost unanimous agreement exists about the classification of Kurmanji and Sorani as Kurdish dialects. The related studies to address the mentioned cases are primarily qualitative. However, computational linguistics could approach the question from a quantitative perspective. In this research, we look into three questions from a linguistic distance point of view. First, how similar or dissimilar Hawrami and Zaza are, considering no common geographical coexistence between the two. Second, what about Kurmanji and Sorani that have geographical overlap. Finally, what is the distance among all these dialects, pair by pair? We base our computation on phonetic presentations of these dialects (languages), and we calculate various linguistic distances among the pairs. We analyze the data and discuss the results to conclude.

cs.CL↗

arXiv2021

Transliterating Kurdish texts in Latin into Persian-Arabic script

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

cs.CL↗

arXiv2020

Leveraging Multilingual News Websites for Building a Kurdish Parallel Corpus

Machine translation has been a major motivation of development in natural language processing. Despite the burgeoning achievements in creating more efficient machine translation systems thanks to deep learning methods, parallel corpora have remained indispensable for progress in the field. In an attempt to create parallel corpora for the Kurdish language, in this paper, we describe our approach in retrieving potentially-alignable news articles from multi-language websites and manually align them across dialects and languages based on lexical similarity and transliteration of scripts. We present a corpus containing 12,327 translation pairs in the two major dialects of Kurdish, Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in English-Kurmanji and English-Sorani. The corpus is publicly available under the CC BY-NC-SA 4.0 license.

cs.CL↗

arXiv2020

Towards Finite-State Morphology of Kurdish

Morphological analysis is the study of the formation and structure of words. It plays a crucial role in various tasks in Natural Language Processing (NLP) and Computational Linguistics (CL) such as machine translation and text and speech generation. Kurdish is a less-resourced multi-dialect Indo-European language with highly inflectional morphology. In this paper, as the first attempt of its kind, the morphology of the Kurdish language (Sorani dialect) is described from a computational point of view. We extract morphological rules which are transformed into finite-state transducers for generating and analyzing words. The result of this research assists in conducting studies on language generation for Kurdish and enhances the Information Retrieval (IR) capacity for the language while leveraging the Kurdish NLP and CL into a more advanced computational level.

cs.CL↗

arXiv2020

Using Punkt for Sentence Segmentation in non-Latin Scripts: Experiments on Kurdish (Sorani) Texts

Segmentation is a fundamental step for most Natural Language Processing tasks. The Kurdish language is a multi-dialect, under-resourced language which is written in different scripts. The lack of various segmented corpora is one of the major bottlenecks in Kurdish language processing. We used Punkt, an unsupervised machine learning method, to segment a Kurdish corpus of Sorani dialect, written in Persian-Arabic script. According to the literature, studies on using Punkt on non-Latin data are scanty. In our experiment, we achieved an F1 score of 91.10% and had an Error Rate of 16.32%. The high Error Rate is mainly due to the situation of abbreviations in Kurdish and partly because of ordinal numerals. The data is publicly available at https://github.com/KurdishBLARK/ KTC-Segmented for non-commercial use under the CC BY-NC-SA 4.0 licence.

cs.CL↗

arXiv2019

Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset

We present an experimental dataset, Basic Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR), which we used in the first attempt in developing an automatic speech recognition for Sorani Kurdish. The objective of the project was to develop a system that automatically could recognize simple sentences based on the vocabulary which is used in grades one to three of the primary schools in the Kurdistan Region of Iraq. We used CMUSphinx as our experimental environment. We developed a dataset to train the system. The dataset is publicly available for non-commercial use under the CC BY-NC-SA 4.0 license.

cs.CL↗

arXiv2019

Developing a Fine-Grained Corpus for a Less-resourced Language: the case of Kurdish

Kurdish is a less-resourced language consisting of different dialects written in various scripts. Approximately 30 million people in different countries speak the language. The lack of corpora is one of the main obstacles in Kurdish language processing. In this paper, we present KTC-the Kurdish Textbooks Corpus, which is composed of 31 K-12 textbooks in Sorani dialect. The corpus is normalized and categorized into 12 educational subjects containing 693,800 tokens (110,297 types). Our resource is publicly available for non-commercial use under the CC BY-NC-SA 4.0 license.

cs.CL↗

arXiv2019

Digital Humanities Readiness Assessment Framework: DHuRAF

This research suggests a framework, Digital Humanities Readiness Assessment Framework (DHuRAF), to assess the maturity level of the required infrastructure for Digital Humanities studies (DH) in different communities. We use a similar approach to the Basic Language Resource Kit (BLARK) in developing the suggested framework. DH as a fairly new field, which has emerged at an intersection of digital technologies and humanities, currently has no framework based on which one could assess the status of the essential elements required for conducting research in a specific language or community. DH offers new research opportunities and challenges in the humanities, computer science and its relevant technologies, hence such a framework could provide a starting point for educational strategists, researchers, and software developers to understand the prerequisites for their tasks and to have a statistical base for their decisions and plans. The suggested framework has been applied in the context of Kurdish DH, considering Kurdish as a less-resourced language. We have also applied the method to the Gaelic language in the Scottish community. Although the research has focused on less-resourced and minority languages, it concludes that DHuRAF has the potential to be generalized in a variety of different contexts. Furthermore, despite significant reliance on Natural Language Processing (NLP) and computational utilities, the research showed that DH could also be used as an essential resource pool to leverage the NLP study of less-resourced and minority languages.

cs.CY↗

arXiv2017

Research Methods in Computer Science: The Challenges and Issues

Research methods are essential parts in conducting any research project. Although they have been theorized and summarized based on best practices, every field of science requires an adaptation of the overall approaches to perform research activities. In addition, any specific research needs a particular adjustment to the generalized approach and specializing them to suit the project in hand. However, unlike most well-established science disciplines, computing research is not supported by well-defined, globally accepted methods. This is because of its infancy and ambiguity in its definition, on one hand, and its extensive coverage and overlap with other fields, on the other hand. This article discusses the research methods in science and engineering in general and in computing in particular. It shows that despite several special parameters that make research in computing rather unique, it still follows the same steps that any other scientific research would do. The article also shows the particularities that researchers need to consider when they conduct research in this field.

cs.GL↗

arXiv2016

Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula

This study extends and evaluates the forecasting performance of the Singular Spectrum Analysis (SSA) technique using a general non-linear form for the re- current formula. In this study, we consider 24 series measuring the monthly seasonally adjusted industrial production of important sectors of the German, French and UK economies. This is tested by comparing the performance of the new proposed model with basic SSA and the SSA bootstrap forecasting, especially when there is evidence of structural breaks in both in-sample and out-of-sample periods. According to root mean-square error (RMSE), SSA using the general recursive formula outperforms both the SSA and the bootstrap forecasting at horizons of up to a year. We found no significant difference in predicting the direction of change between these methods. Therefore, it is suggested that the SSA model with the general recurrent formula should be chosen by users in the case of structural breaks in the series.

q-fin.ST↗

arXiv2014

On the folded normal distribution

The characteristic function of the folded normal distribution and its moment function are derived. The entropy of the folded normal distribution and the Kullback--Leibler from the normal and half normal distributions are approximated using Taylor series. The accuracy of the results are also assessed using different criteria. The maximum likelihood estimates and confidence intervals for the parameters are obtained using the asymptotic theory and bootstrap method. The coverage of the confidence intervals is also examined.

stat.ME↗