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Morphological Segmentation and Part-of-Speech Tagging for the Arabic Heritage

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Emad MohamedAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 17, Issue 3

Article No.: 22, Pages 1 - 13

https://doi.org/10.1145/3178459

Published: 02 April 2018 Publication History

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Abstract

We annotate 60,000 words of Classical Arabic (CA) with topics in philosophy, religion, literature, and law with fine-grain segment-based morphological descriptions. We use these annotations for building a morphological segmenter and part-of-speech (POS) tagger for CA. With character-level classification and features from the word and its lexical context, the segmenter achieves a word accuracy of 96.8% with the main issue being a high rate of out-of-vocabulary words. A token-based POS tagger achieves an accuracy of 96.22% with 97.72% on known tokens despite the small size of the corpus. An error analysis shows that most of the tagging errors are results of segmentation and that quality improves with more data being added. The morphological segmenter and tagger have a wide range of potential applications in processing CA, a low-resource variety of the language.

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Index Terms

Morphological Segmentation and Part-of-Speech Tagging for the Arabic Heritage
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
      2. Phonology / morphology

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Published In

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 17, Issue 3

September 2018

196 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3184403

Editor:
Nianwen Xue
Brandeis University, Waltham, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 April 2018

Accepted: 01 December 2017

Revised: 01 December 2017

Received: 01 June 2015

Published in TALLIP Volume 17, Issue 3

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Ding Q(2022)[Retracted] Analysis of the Impact of the Traditional Literature Environment Based on Big Data Technology on Overseas LiteratureJournal of Environmental and Public Health10.1155/2022/28343632022:1Online publication date: 30-Sep-2022
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M PJ HT BS VN IM JK I(2022)CNN Based Character Recognition and Classification in Tamil Palm Leaf Manuscripts2022 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IOT53935.2022.9767866(1-6)Online publication date: 10-Mar-2022
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Zhang XGao Y(2020)RETRACTED ARTICLE: Multimedia text classification algorithm using potential Dirichlet distribution in mobile cloud computing environmentMultimedia Tools and Applications10.1007/s11042-019-08253-179:13-14(9615-9627)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1007/s11042-019-08253-1
AbuZeina DAbdalbaset TAbu Arqub O(2019)Exploring the Performance of Tagging for the Classical and the Modern Standard ArabicAdvances in Fuzzy Systems10.1155/2019/62546492019Online publication date: 23-Jan-2019
https://dl.acm.org/doi/10.1155/2019/6254649
Mohamed ESayyed Z(2019)Arabic-SOSProceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage10.1145/3322905.3322927(27-32)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.1145/3322905.3322927

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Exploiting Separation of Closed-Class Categories for Arabic Tokenization and Part-of-Speech Tagging

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