Research Article Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic Dia AbuZeina 1 and Taqieddin Mostafa Abdalbaset 2 1 College of Information Technology and Computer Engineering, Palestine Polytechnic University, Hebron, State of Palestine 2 Palestine Technical University–Kadoorie, AL-Aroub Branch, Hebron, State of Palestine Correspondence should be addressed to Dia AbuZeina; abuzeina@ppu.edu Received 7 August 2018; Accepted 23 October 2018; Published 23 January 2019 Guest Editor: Omar Abu Arqub Copyright © 2019 Dia AbuZeina and Taqieddin Mostafa Abdalbaset. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te 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. Te 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. Tis result promotes the need for further research to expose why the tagging is severely inaccurate for classical Arabic. Te performance decline might be an indication of the necessity to distinguish between training data for both classical and MSA Arabic for NLP tasks. 1. Introduction Te part of speech (PoS) tagging, also known as word- category disambiguation, is a process to determine the tag of each word in a given input text. Te tagging process uses the context to label words using syntactic tags, such as noun, adjective, verb, or preposition that are also known as parts of speech, word-classes, grammatical categories, lexical class markers, or syntactic categories. Tagging is performed either manually by linguistic experts or automatically by machine learning algorithms; intuitively, this work considers the computational track. Word tags are mainly used to describe the words and their jobs according to the context for further processing. Tat is, each word has a particular role based on the position and the adjacent words in the sentence. Te tagset is a predefned list that generally includes some symbols, such as nouns, pronouns, adjectives, verbs, adverbs, propositions, conjunctions, and the defnite and indefnite articles (sometimes called “determiners”). Of course, the tagset is prepared by the language linguistic scholars to describe the language’s membership or word family. Te size of the tagset is variable and depends on the requirements or the capacity of developing applications. In any case, the tagset should best ft and efciently serve the intended purposes. Hence, there is no predefned tagset for all languages and thereisnostandard(i.e.,unique)tagsetforacertainlanguage. Rather, it is a debatable matter. Te PoS is increasingly becoming a vital factor in the related natural language processing (NLP) applications. In fact, creating knowledge base resources (e.g., tag relation- ships) is one objective of the PoS tagging that can be later used in other NLP tools. In fact, PoS tagging has many roles in the feld of NLP as a basic prepossessing step. For instance, some of NLP PoS tagging based applications include syntactic parsing, information extraction, machine translation, speech synthesis, and named entity recognition (NER). Tis work is aimed at exploring the performance of the PoS for the classical Arabic using a modern standard Arabic (MSA) tagger that is the Stanford tagger [1]. Since it is difcult to evaluate the Stanford tagger for all tags (29 tags) as it requires Hindawi Advances in Fuzzy Systems Volume 2019, Article ID 6254649, 10 pages https://doi.org/10.1155/2019/6254649