Arabic Morphological Analyzer Research Articles

Ibn-Ginni: An Improved Morphological Analyzer for Arabic

Arabic is a morphologically rich language, which means that the Arabic language has a complicated system of word formation and structure. The affixes in the Arabic language (i.e., prefixes and suffixes) can be added to root words to generate different meanings and grammatical functions. These affixes can indicate aspects such as tense, gender, number, case, person, and more. In addition, the meaning and function of words can be modified in Arabic using an internal structure known as morphological patterns. Computational morphological analyzers of Arabic are vital to developing Arabic language processing toolkits. In this article, we introduce a new morphological analyzer (Ibn-Ginni) that inherits the speed and quality of the Buckwalter Arabic Morphological Analyzer (BAMA). The BAMA has poor coverage of the classical Arabic language. Hence, the coverage of classical Arabic is improved by using the Alkhalil analyzer. Although it is slow, it was used to generate a huge number of solutions for 3 million unique Arabic words collected from different resources. These word form-based solutions were converted to stem-based solutions, refined manually, and added to the database of BAMA, resulting in substantial improvements in the quality of the analysis. Hence, Ibn-Ginni is a hybrid system between BAMA and Alkhalil analyzers and may be considered an efficient large-scale analyzer. The Ibn-Ginni analyzer analyzed 0.6 million more words than the BAMA analyzer. Therefore, our analyzer significantly improves the coverage of the Arabic language. Besides, the Ibn-Ginni analyzer is high speed at providing solutions, the average time to analyze a word is 0.3 ms. Using a corpus designed for benchmarking Arabic morphological analyzers, our analyzer was able to find all solutions for 72.72% of the words. Moreover, the analyzer did not provide all possible morphological solutions for 24.24% of the words. The analyzer and its morphological database are publicly available on GitHub. 1

Integrated approaches in a morphological analyzer of the Arabic language

This article presents a systemof a morphological analyzer of the Arabic language, by integrating several approaches and the viterbi algorithm. First approach is based on database for all thesurface patterns in the Arabic language, second approach is Buckwalter Arabic morphological analyzer and the last approach is based on finite state automaton. With the integration of correspondence tables between affixes in these approaches. The combination between these approaches in our analyzer is very important. Our analyzer is tested on a morphological corpus of 200,000 words, which generalize the words of the Arabic language. The effectiveness of the proposed approaches is demonstrated experimentally and the results obtained are comparable to the state of the art. Moreover, it shows the interest and the advantages of integrating these approaches are to improve our morphological analyzer.

A Computational Analysis of Arabic Noun Morphology

This project looks at Arabic word generation from a computational angle. It focuses on the computational production and analysis of morphological Arabic nouns. The work begins with a stem-based descriptive analysis of Arabic noun morphology that fulfills both the computational formalization and the linguistic description. There includes a thorough discussion of both inflectional and derivational systems. The spelling of Arabic nouns is also covered, as well as morphotactics and morphophonemics. The work then offers a computer implementation of Arabic nouns built on a rule-based computational morphological methodology. The overall system is constructed using the NooJ toolkit, which supports both pushdown automata and finite-state automata (FSA) (PDA). Three elements make up the morphological generation and analysis system: a lexicon, morphotactics, and rules. The lexicon component catalogs lexical elements (indivisible words and affixes), the morphotactics component specifies ordering restrictions for morphemes, and the rules component converts lexical representations into surface representations and vice versa. Other rules, such as orthographic, morphophonemic, and morphological rules, are also stored as two-level rules. The core editable lexicon of lemmas used as input by the system is drawn from three sources: the Buckwalter Arabic morphological analyzer lexicon, the Arramooz machine-readable dictionary, and the Alghani Azzahir dictionary. A complete annotated vocabulary of inflected noun forms (combined into a single type of finite-state transducers (FSTs)) is the system's output. The lexicon that was developed is then put to use in morphological analysis. The study then offers the system's evaluation. Accuracy, precision, and recall are three widely used metrics to assess the system's performance. Two empirical experiments will be conducted as part of the evaluation task. The system analyzing Arabic words that have been discredited morphologically is evaluated in the first experiment. Accuracy, precision, and recall for the system when employing discredited Arabic words are (90.4%), (98.3%), and (88.9%), respectively. The technique is tested in a second experiment using undiacritical words. The achieved outcomes of this experiment were (94.7%) accuracy, (96.7%) precision, and (91.6% ) recall, respectively. Additionally, the measurement average for the two tests has been determined. The average performance values are respectively (92.55%), (97.5%), and (90.25) percent in terms of recall, precision, and accuracy. Overall, the results are encouraging and demonstrate the system's propensity for dealing with both diacritically and undiacritically written Arabic texts. This system can analyze Arabic text corpora in-depth and tag nouns according to their morphological characteristics. It breaks the word under analysis into three pieces (the stem, proclitics/prefixes, and suffixes/enclitics) and assigns each one a specific morphological feature tag or possibly many tags if the portion in question has numerous clitics or affixes. Many applications of natural language processing, including parsing, lemmatization, stemming, part-of-speech (POS) tagging, corpus annotations, word sense disambiguation, machine translation, information retrieval, text generation, spelling checkers, etc., depend on computational morphology. It is made up of morphological generation and analysis paradigms. According to a set of features, morphological generation attempts to construct every feasible derived and inflected form of a given lemma. On the other hand, morphological analysis is the process of dissecting a word into its component morphemes and giving each morpheme linguistic tags or qualities.

The Use of the Relational Concept in the Arabic Morphological Analysis

The Arabic language differs from other natural languages in its structures and compositions. In this article we have developed an Arabic morphological analyzer. For this, we have used the relational concept in the database to build our Arabic morphological analyzer. This analyzer uses a set of tables which are linked together by relationships. These relations model certain numbers of compatibility rules between different affixes. Our morphological analysis have been trained and tested on the same databases. The tests of our new approach have given good results and the numbers obtained are very close to those of existing analyzer.

A Link Prediction Approach for Accurately Mapping a Large-scale Arabic Lexical Resource to English WordNet

Success of Natural Language Processing (NLP) models, just like all advanced machine learning models, rely heavily on large -scale lexical resources. For English, English WordNet (EWN) is a leading example of a large-scale resource that has enabled advances in Natural Language Understanding (NLU) tasks such as word sense disambiguation, question answering, sentiment analysis, and emotion recognition. EWN includes sets of cognitive synonyms called synsets, which are interlinked by means of conceptual-semantic and lexical relations and where each synset expresses a distinct concept. However, other languages are still lagging behind in having large-scale and rich lexical resources similar to EWN. In this article, we focus on enabling the development of such resources for Arabic. While there have been efforts in developing an Arabic WordNet (AWN), the current version of AWN has its limitations in size and in lacking transliteration standards, which are important for compatibility with Arabic NLP tools. Previous efforts for extending AWN resulted in a lexicon, called ArSenL, that overcame the size and the transliteration standard limitation but was limited in accuracy due to the heuristic approach that only considered surface matching between the English definitions from the Standard Arabic Morphological Analyzer (SAMA) and EWN synset terms, and that resulted in inaccurate mapping of Arabic lemmas to EWN’s synsets. Furthermore, there has been limited exploration of other expansion methods due to expensive manual validation needed. To address these limitations of simultaneously having large-scale size with high accuracy and standard representations, the mapping problem is formulated as a link prediction problem between a large-scale Arabic lexicon and EWN, where a word in one lexicon is linked to a word in another lexicon if the two words are semantically related. We use a semi-supervised approach to create a training dataset by finding common terms in the large-scale Arabic resource and AWN. This set of data becomes implicitly linked to EWN and can be used for training and evaluating prediction models. We propose the use of a two-step Boosting method, where the first step aims at linking English translations of SAMA’s terms to EWN’s synsets. The second step uses surface similarity between SAMA’s glosses and EWN’s synsets. The method results in a new large-scale Arabic lexicon that we call ArSenL 2.0 as a sequel to the previously developed sentiment lexicon ArSenL. A comprehensive study covering both intrinsic and extrinsic evaluations shows the superiority of the method compared to several baseline and state-of-the-art link prediction methods. Compared to previously developed ArSenL, ArSenL 2.0 included a larger set of sentimentally charged adjectives and verbs. It also showed higher linking accuracy on the ground truth data compared to previous ArSenL. For extrinsic evaluation, ArSenL 2.0 was used for sentiment analysis and showed, here, too, higher accuracy compared to previous ArSenL.

Qillah: A Morphological Extension for Identifying Plural-of-Paucity Arabic Words

Plural of paucity is one type of broken plural used in the classical Arabic. It is used when the number of people or objects ranges from three to 10. Based on our evaluation of four current state-of-the-art Arabic morphological analyzers, there is a lack of identification of broken plural words, specifically the plural of paucity. Therefore, this paper presents Qillah (paucity), a morphological extension that is built on top of other morphological analyzers and uses a hybrid rule-based and lexicon-based approach to enhance the identification of plural of paucity. Two versions of the Qillah were developed, one is based on FARASA morphological analyzer and the other is based on CALIMA Star analyzer, as these are some of the best-performing morphological analyzers. We designed two experiments to evaluate the effectiveness of our proposed solution based on a collection of 402 different Arabic words. The version based on CALIMA Star achieved a maximum accuracy of 93% in identifying the plural-of-paucity words compared to the baselines. It also achieved a maximum accuracy of 98% compared to the baselines in identifying the plurality of the words.

Arabic Morphological Analysis Techniques

Recently, activity surrounding Arabic natural language processing has increased significantly. Morphological analysis is the basis of most tasks related to Arabic natural language processing. There are many scientific studies on Arabic morphological analysis, yet most of them lack an accurate classification of Arabic morphology and fail to cover both recent and traditional techniques. This paper aims to survey Arabic morphological analysis techniques from 2005 to 2019 and to organize them into a reasonable and expandable classification system. To facilitate and support new research, this paper compares the currently available Arabic morphological analyzers, reaches certain conclusions, and proposes some promising directions for future research in Arabic morphological analysis.

Arabic Solid-Stems for an Efficient Morphological Analysis

Arabic natural language processing “NLP” researchers have not yet reached a consensus on a unified definition of stop-words, which is a challenging issue and a limitation in the domain of Arabic NLP in general and Arabic morphological analysis in particular. In this research work, we start by giving a detailed definition and classification of solid-stem words which renames stop-words; we then propose a linguistic-based morphological analysis approach to process this class of words in the Arabic language. A solid-stem word has a unique morphological form and is characterized by the fact that the inflectional ending of the word does not change no matter where the word is in the sentence. A solid-stem word can be a constructed noun (pronoun, indeclinable noun, and verbal noun), an invariable verb, or a particle. A new classification of solid-stems is given, based on their type of affixations. The proposed approach distinguishes between variable and invariable solid-stem types, identifies all possible affixes, and generates all possible morphological variants of each word in a systematic way. For this purpose, we propose a formula for solid-stem affixation and describe in detail affixes schemas using finite-state machine. Building the work on strong linguistic bases lays the foundation for building efficient Arabic search engines and special-purpose information retrieval systems.

PEMBELAJARAN BAHASA ARAB DENGAN PENDEKATAN ANALISIS MORFOLOGI

Arabic is a series of operations and processes train students to familiarize themselves to acquire skills / ability to understand everything that is heard, speaking, reading and writing in Arabic. One of proficiency is the skill to read and understand Arabic texts properly. However, in the practical realm is still encountered some problems and issues in this maharoh learning, students are still difficult to read or pronounce the word / phrase and find its meaning. This is because many students do not recognize and pronounce unfamiliar words based wazan (pronunciation rules) and sighot (type of word based dilalahnya). Of this problem researchers provide an alternative solution by presenting a pattern-based learning Arabic morphological analyzer. The objectives of this study were: 1) describe the procedures / technical-based learning Arabic morphology. 2) measure the effectiveness of pattern-based learning Arabic morphological analyzer in increasing reading competence. This study using a pre experimental design with a form of one-shot case Study. And the results of this study concluded that the pattern of learning is quite effective with indicators: 1) students are familiar and easy to pronounce and utter words corresponding wazan / shighot. 2) student easily to memory on vocabulary acquisition; origin of word and its meaning changes. 3) the students more easily and quickly understand a text with the stock of the results of the acquisition of vocabulary and understanding of the meaning of the vocabulary.Keywords: Learning Arabic, Morphology, Reading skill.

Improving Arabic morphological analyzers benchmark

The various tools dedicated to Arabic natural language processing have undergone significant development during recent years. Among these tools, Arabic morphological analyzers are of great importance because they are often used within other projects that are more advanced such as syntactic parsers, search engines, machine translation systems, etc. Thus, researchers are forced to make a decision concerning which morphological analyzer to use in their research projects, and this task is very difficult since there are many criteria to take into account. In order to facilitate this choice, we considered the problem of benchmarking morphological analyzers in a previous work by proposing a solution that allows returning a set of metrics of each analyzer that are: accuracy, precision, recall, F-measure and the execution time. In this article, we present two new major improvements to our solution: the establishment of the first version of our corpus that is dedicated to the evaluation of morphological analyzers, as well as the introduction of a new metric, which combines all metrics related to results as well as the execution time of the analyzers.

Morphological Analysis in the Environment “TELA”

Abstract In this article we described the approach to achieving Telama analyzer (Towards Environmental-Learning Arabic-Morphological Analyzer): statistical approach based on the Hidden Markov Model (MMC) of Degree 2 and Trigrams. We describe the general algorithm and resources performed to accomplish the task of achieving the analyzer. This work also details the different experiments: the problem of analyzing the training corpus and selection of the set of labels. Finally, an evaluation of our segmentation system closes this stage of work on the morphological analysis of Arabic words, the results are very encouraging.

Assessment Criteria for Benchmarking Arabic Morphological Analyzers and Generators

Natural language processing applications are based on the morphology part. So they should meet some criteria in order to satisfy the required functionality. Assessing and evaluating of Arabic morphological systems depend on the input words and resulted output according to a predefined criteria to measure and analyze given system in order to study its weakness and strength, trying to find an Arabic morphological analyzer free from all mistakes. In this paper we developed the precise assessment criteria for Arabic morphological analyzers to be applied on a given Arabic morphological analyzers and stemming algorithms by voting, after running them on the sample documents selected as the gold standard.

Theoretical and Computational Perspectives of Arabic Morphological Analyzers and Generators : Theoretical Survey

Morphology analysis is an essential part of most applications of natural language processing (NLP) which included different applications like Machine Translation (MT) and language rule based Information Retrieval (IR). Many Arabic morphological systems had built for different purposes with different algorithms and approaches; this paper is considered a survey of Arabic Morphological system from researchers’ perspectives and approaches used to build them. Based on this survey; in the first part of this paper; the perspective of Arabic morphological systems had been classified into two major issues; one of them is the theoretical perspective and the second is the computational perspective of Arabic morphology. While the second part of this paper deals with approaches used to build the Arabic Morphology systems itself which are Table Lookup Approach, Combinatorial Approach, Linguistic Approach, Traditional Approaches, Finite-state Automata and Two-Level Morphology Approach and Pattern-Based Approach.

An Improved General Purpose Arabic Morphological Analyzer and Generator Model (GPAM)

Although, morphological analysis is a vital part of natural language processing applications, there are no definitive standards for evaluating and benchmarking Arabic morphological systems. This paper proposes assessment criteria for evaluating Arabic morphological systems by scrutinizing the input, output and architectural design to enables researchers to evaluate and fairly compare Arabic morphology systems. By scoring some state of the art Arabic morphological analyzers based on the proposed criteria; the accuracy scores showed that the best algorithm failed to achieve a reliable rate. Hence, this paper introduced an enhanced algorithm for resolving the inflected Arabic word, identifies its root, finds its pattern and POS tagging that will reduce the search time considerably and to free up the deficiencies identified by this assessment criteria. The proposed model uses semantic rules of the Arabic language on top of a hybrid sub-model based on two existing algorithms (Al-Khalil & IAMA rules). Based on applying the proposed assessment criteria the efficiency and speed have been enhanced where the system achieved up to 1500 words per second in small text up to 3000 words per second in larger documents

Analysis and feedback of erroneous Arabic verbs

AbstractArabic language is strongly structured and considered as one of the most highly inflected and derivational languages. Learning Arabic morphology is a basic step for language learners to develop language skills such as listening, speaking, reading, and writing. Arabic morphology is non-concatenative and provides the ability to attach a large number of affixes to each root or stem that makes combinatorial increment of possible inflected words. As such, Arabic lexical (morphological and phonological) rules may be confusing for second language learners. Our study indicates that research and development endeavors on spelling, and checking of grammatical errors does not provide adequate interpretations to second language learners’ errors. In this paper we address issues related to error diagnosis and feedback for second language learners of Arabic verbs and how they impact the development of a web-based intelligent language tutoring system. The major aim is to develop an Arabic intelligent language tutoring system that solves these issues and helps second language learners to improve their linguistic knowledge. Learners are encouraged to produce input freely in various situations and contexts, and are guided to recognize by themselves the erroneous functions of their misused expressions. Moreover, we proposed a framework that allows for the individualization of the learning process and provides the intelligent feedback that conforms to the learner's expertise for each class of error. Error diagnosis is not possible with current Arabic morphological analyzers. So constraint relaxation and edit distance techniques are successfully employed to provide error-specific diagnosis and adaptive feedback to learners. We demonstrated the capabilities of these techniques in diagnosing errors related to Arabic weak verbs formed using complex morphological rules. As a proof of concept, we have implemented the components that diagnose learner's errors and generate feedback which have been effectively evaluated against test data acquired from real teaching environment. The experimental results were satisfactory, and the performance achieved was 74.34 percent in terms of recall rate.

Fassieh¯, a Semi-Automatic Visual Interactive Tool for Morphological, PoS-Tags, Phonetic, and Semantic Annotation of Arabic Text Corpora

This paper introduces an Arabic text annotation tool called Fassieh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> . Via a sophisticated interactive GUI application, Fassieh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> makes it easy to build structured large standard written Arabic corpora, then allows the production of fundamental linguistic analyses; i.e., language factorizations, at high coverage and accuracy rates over such corpora. Arabic morphological analysis, part-of-speech (PoS)-tagging, full phonetic transcription (diacritization), and lexical semantics analysis are the most significant Arabic language factorizations currently supported by Fassieh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> . The high inherent ambiguity of these analyses is statistically resolved in Fassieh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> which also affords a multitude of auxiliary features enabling a guided, normalized, and efficient proofreading of any part of the factorized corpus. The paper first reviews the highly inflective and derivative nature of Arabic language, our Arabic language factorization models, and the associated statistical disambiguation methodology. Afterwards, we present Fassieh <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reg</sup> which is not only a text annotation tool, but is also an evaluation, demonstrative, and tutorial means of Arabic natural language processing (NLP).

Arabic morphological analysis techniques: A comprehensive survey

AbstractAfter several decades of heavy research activity on English stemmers, Arabic morphological analysis techniques have become a popular area of research. The Arabic language is one of the Semitic languages; it exhibits a very systematic but complex morphological structure based on root‐pattern schemes. As a consequence, survey of such techniques proves to be more necessary. The aim of this paper is to summarize and organize the information available in the literature in an attempt to motivate researchers to look into these techniques and try to develop more advanced ones. This paper introduces, classifies, and surveys Arabic morphological analysis techniques. Furthermore, conclusions, open areas, and future directions are provided at the end.

An LVQ connectionist solution to the non-determinacy problem in Arabic morphological analysis: a learning hybrid algorithm

Most of the morphological properties of derivational Arabic words are encapsulated in their corresponding morphological patterns. The morphological pattern is a template that shows how the word should be decomposed into its constituent morphemes (prefix + stem + suffix), and at the same time, marks the positions of the radicals comprising the root of the word. The number of morphological patterns in Arabic is finite and is well below 1000. Due to these properties, most of the current analysis algorithms concentrate on discovering the morphological pattern of the input word as a major step in recognizing the type and category of the word. Unfortunately, this process is non-determinitic in the sense that the underlying search process may sometimes associate more than one morphological pattern with the given word, all of them satisfying the major lexical constraints. One solution to this problem is to use a collection of connectionist pattern associaters that uniquely associate each word with its corresponding morphological pattern. This paper describes an LVQ-based learning pattern association system that uniquely maps a given Arabic word to its corresponding morphological pattern, and therefore deduces its morphological properties. The system consists of a collection of hetroassociative models that are trained using the LVQ algorithm plus a collection of autoassociative models that have been trained using backpropagation. Experimental results have shown that the system is fairly accurate and very easy to train. The LVQ algorithm has been chosen because it is very easy to train and the implied training time is very small compared to that of backpropagation.