Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic
The part of speech (PoS) tagging is a core component in many natural language processing (NLP) applications. In fact, the PoS taggers contribute as a preprocessing step in various NLP tasks, such as syntactic parsing, information extraction, machine translation, and speech synthesis. In this paper,...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2019/6254649 |
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| author | Dia AbuZeina Taqieddin Mostafa Abdalbaset |
| author_facet | Dia AbuZeina Taqieddin Mostafa Abdalbaset |
| author_sort | Dia AbuZeina |
| collection | DOAJ |
| description | The part of speech (PoS) tagging is a core component in many natural language processing (NLP) applications. In fact, the PoS taggers contribute as a preprocessing step in various NLP tasks, such as syntactic parsing, information extraction, machine translation, and speech synthesis. In this paper, we examine the performance of a modern standard Arabic (MSA) based tagger for the classical (i.e., traditional or historical) Arabic. In this work, we employed the Stanford Arabic model tagger to evaluate the imperative verbs in the Holy Quran. In fact, the Stanford tagger contains 29 tags; however, this work experimentally evaluates just one that is the VB ≡ imperative verb. The testing set contains 741 imperative verbs, which appear in 1,848 positions in the Holy Quran. Despite the previously reported accuracy of the Arabic model of the Stanford tagger, which is 96.26% for all tags and 80.14% for unknown words, the experimental results show that this accuracy is only 7.28% for the imperative verbs. This result promotes the need for further research to expose why the tagging is severely inaccurate for classical Arabic. The performance decline might be an indication of the necessity to distinguish between training data for both classical and MSA Arabic for NLP tasks. |
| format | Article |
| id | doaj-art-ee48a026b95345faa78937c637f4f76c |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-ee48a026b95345faa78937c637f4f76c2025-02-03T00:59:50ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2019-01-01201910.1155/2019/62546496254649Exploring the Performance of Tagging for the Classical and the Modern Standard ArabicDia AbuZeina0Taqieddin Mostafa Abdalbaset1College of Information Technology and Computer Engineering, Palestine Polytechnic University, Hebron, State of PalestinePalestine Technical University–Kadoorie, AL-Aroub Branch, Hebron, State of PalestineThe part of speech (PoS) tagging is a core component in many natural language processing (NLP) applications. In fact, the PoS taggers contribute as a preprocessing step in various NLP tasks, such as syntactic parsing, information extraction, machine translation, and speech synthesis. In this paper, we examine the performance of a modern standard Arabic (MSA) based tagger for the classical (i.e., traditional or historical) Arabic. In this work, we employed the Stanford Arabic model tagger to evaluate the imperative verbs in the Holy Quran. In fact, the Stanford tagger contains 29 tags; however, this work experimentally evaluates just one that is the VB ≡ imperative verb. The testing set contains 741 imperative verbs, which appear in 1,848 positions in the Holy Quran. Despite the previously reported accuracy of the Arabic model of the Stanford tagger, which is 96.26% for all tags and 80.14% for unknown words, the experimental results show that this accuracy is only 7.28% for the imperative verbs. This result promotes the need for further research to expose why the tagging is severely inaccurate for classical Arabic. The performance decline might be an indication of the necessity to distinguish between training data for both classical and MSA Arabic for NLP tasks.http://dx.doi.org/10.1155/2019/6254649 |
| spellingShingle | Dia AbuZeina Taqieddin Mostafa Abdalbaset Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic Advances in Fuzzy Systems |
| title | Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic |
| title_full | Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic |
| title_fullStr | Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic |
| title_full_unstemmed | Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic |
| title_short | Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic |
| title_sort | exploring the performance of tagging for the classical and the modern standard arabic |
| url | http://dx.doi.org/10.1155/2019/6254649 |
| work_keys_str_mv | AT diaabuzeina exploringtheperformanceoftaggingfortheclassicalandthemodernstandardarabic AT taqieddinmostafaabdalbaset exploringtheperformanceoftaggingfortheclassicalandthemodernstandardarabic |