Towards an Optimal Solution to Lemmatization in Arabic

Freihat, Abed Alhakim; Abbas, Mourad; Bella, Gábor; Giunchiglia, Fausto

doi:10.1016/j.procs.2018.10.468

Cited by 18 publications

(5 citation statements)

References 9 publications

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“…(Plisson et al 2004;Stanković et al 2016;Nandathilaka, Ahangama, and Weerasuriya 2018)), machine learning-based (e.g. (Kestemont et al 2017;Freihat et al 2018;Manjavacas, Kadar, and Kestemont 2019;Akhmetov et al 2020;Karwatowski and Pietron 2022;Hafeez et al 2023)) or they employ a combination of these methods and represent hybrid models (e.g. (Ingason et al 2008;Sahala et al 2023)).…”

Section: Lemmatizationmentioning

confidence: 99%

“…Even though many traditional machine learning techniques have been used for lemmatization throughout the years (e.g. random forest classification model (Akhmetov et al 2020) and maximum entropy classifier (Freihat et al 2018)), it seems that the researchers have lately been more oriented towards the deep learning-based approaches (e.g. (Kestemont et al 2017; Manjavacas, Kadar, and Kestemont 2019; Ezhilarasi and Maheswari 2021b; Karwatowski and Pietron 2022;Hafeez et al 2023)).…”

Section: Lemmatizationmentioning

confidence: 99%

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A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Braović,

Krstinić,

Štula

et al. 2024

Computational Linguistics

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This paper provides a detailed insight into computational approaches for deciphering Bronze Age Aegean and Cypriot scripts, namely the Archanes script and the Archanes formula, Phaistos Disk, Cretan hieroglyphic (including the Malia Altar Stone and Arkalochori Axe), Linear A, Linear B, Cypro-Minoan and Cypriot scripts. The unique contributions of this paper are threefold: 1) a thorough review of major Bronze Age Aegean and Cypriot scripts and inscriptions, digital data and corpora associated with them, existing computational decipherment methods developed in order to decipher them, and possible links to other scripts and languages; 2) the definition of 15 major challenges that can be encountered in computational decipherments of ancient scripts; and 3) an outline of a computational model that could possibly be used to simulate traditional decipherment processes of ancient scripts based on palaeography and epigraphy. In the context of this paper the term decipherment denotes the process of discovery of the language and/or the set of symbols behind an unknown script, and the meaning behind it.

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Section: Lemmatizationmentioning

confidence: 99%

Section: Lemmatizationmentioning

confidence: 99%

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Braović,

Krstinić,

Štula

et al. 2024

Computational Linguistics

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“…It can be done instead of applying stemming. Lemmatization is the process of assigning a corresponding lemma to each surface form of a word in a text, that is, the canonical form of the word as it appears in a dictionary (Freihat et al, 2018). Since of the rich morphology of Arabic, lemmatization is a complex task.…”

Section: ‫'إن'‬ ‫'إذن'‬ ‫'إذا'‬mentioning

confidence: 99%

Twitter Sentiment Analysis for Reviewing Tourist Destinations in Saudi Arabia using Apache Spark and Machine Learning Algorithms

Alasmari¹,

Abdelhafez²

2022

Journal of Computer Science

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The appearance of big data has created new challenges for data analysis teams especially dealing with unstructured data in text form. Many applications increasingly include a large amount of this type of data. Example of such data is data collected from Twitter. Adequate use of Machine Learning (ML), big data tools and social media platforms can solve several problems. The aim of this research is to apply sentiment analysis using Arabic tweets of tourism in Saudi Arabia and determine the most visited places. Ara Senti corpus was used as the labelled data to perform machine learning for sentiment analysis to deal with the Arabic morphology. The three-classes classification (Positive, Negative, or Neutral) was performed using Decision Tree, Random Forest, Logistic Regression and Naïve Bayes. The results showed that the highest performance achieved was 86% using Logistic Regression with Term Frequency-Inverse Document Frequency (TF-IDF) representation and Naïve Bayes with Bag-of-Words model compared with both random forest and decision tree. The trainable classifier was applied to predict classes on collected data from Twitter for reviewing Kingdom of Saudi Arabia (KSA) destinations to finally present a rating of the most visited places on KSA. There are five most visited places in Saudi Arabia (Riyadh, Alula, Hail, Taif and Tabuk).

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“…Lemmatization takes a more complex approach in text processing; It aims to regroup semantically related words, and it is proved to be beneficial in the areas of Arabic information retrieval [11], [14]. However, in Arabic, the use of lemmatization is more difficult task due to the morphological complexity of the language itself, and the absence of short vowels in most existing Arabic documents [15].…”

Section: Lemmatizationmentioning

confidence: 99%

Review on Recent Arabic Information Retrieval Techniques

AARAB

Oussous

Saddoune

2022

EAI Endorsed Trans IoT

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Information retrieval is an important field that aims to provide a relevant document to a user information need, expressed through a query. Arabic is a challenging language that gained much attention recently in the information retrieval domain. To overcome the problems related to its complexity, many studies and techniques have been presented, most of them were conducted to solve the stemming problem. This paper presents an overview of the Arabic information retrieval process, including various text processing techniques, ranking approaches, evaluation measures, and some important information retrieval models. The paper finally presents some recent related studies and approaches in different Arabic information retrieval fields.

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Towards an Optimal Solution to Lemmatization in Arabic

Cited by 18 publications

References 9 publications

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Twitter Sentiment Analysis for Reviewing Tourist Destinations in Saudi Arabia using Apache Spark and Machine Learning Algorithms

Review on Recent Arabic Information Retrieval Techniques

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