Arabic Language Characteristics that Make its Automatic Processing Challenging

The legal sector remains distinctive due to the complex language structure and specialized terminology of legal data. This complexity offers considerable contextual information, which demands natural language processing (NLP). The availability of high-quality and well-structured legal datasets is essential for advancing NLP research and applications within the legal field. However, a gap exists within the Arabic legal NLP owing to insufficient research and datasets. To address this gap, we aim to propose an Arabic legal case dataset containing cases, case summaries, relevant keywords, and case categories. The legal case data were obtained from the Board of Grievances website in Saudi Arabia and include 3170 cases distributed across 47 classes. The number of words in these cases varies significantly, ranging from about 100 to nearly 30,000 words per case. Moreover, the number of pages varies, ranging from one page to 80 pages per case. Therefore, this dataset supports various NLP applications, including text categorization, data extraction, sentiment analysis, and summarization, thereby improving task efficiency and decision accuracy in the legal profession.

Arabic natural language processing (ANLP) consists of developing techniques and tools that can utilize and analyze the Arabic language in both written and spoken contexts. ANLP makes an important contribution to many existing developed systems. It provides Arabic and non-Arabic speakers with helpful and convenient tools that can be used in different domains. Modern ANLP tools are developed using machine learning (ML) techniques. ML algorithms are widely used in NLP because of their high accuracy rate regardless of the robustness of the data that is used and because of the ease with which they can be implemented. On the other hand, the methodology of ANLP applications based on ML involves several distinct phases. It is, therefore, crucial to recognize and understand these phases in detail as well as the most widely used ML algorithms. This survey discusses this concept in detail, shows the involvement of ML techniques in developing such tools, and identifies well-known techniques used in ANLP. Moreover, this survey discusses the characteristics and complexity of the Arabic language in addition to the importance and needs of ANLP.

Morphologically complex languages such as Arabic pose several challenges in Natural Language Processing (NLP) due to their complexity and token sparsity. Most techniques approach the problem by transforming the words of the language from their sparse surface form representation to a less sparse form before processing. The transformation usually takes the form of a morphological analysis or a morphological segmentation. This dissertation addresses two tasks in Arabic NLP: Statistical Machine Translation(SMT) and Sentiment Analysis. To improve English-Arabic SMT, we apply segmentation on Arabic to decrease token sparsity and enhance the correspondence between tokens of the English and Arabic language. However, due to this segmentation, the translation system is limited to extracting features based on morphemes (partial words) and only outputting morphemes during decoding. Such a system lacks knowledge of the original form of the words. We further improve translation from English to Arabic by combining both segmented and desegmented views of the target language. The system can benefit from segmentation's sparsity reduction and verifies its generation of correct words. We present a language-independent technique to desegmentation that approaches the problem as a string transduction task. We propose a new algorithm that desegments the decoder's search space encoded as a lattice, thus allowing the system to use features from the desegmented view of the search space. We extend the phrase-based statistical machine translation system to allow desegmentation during the decoding process on the fly. In addition, we conduct an experimental study to verify what matters most in morphologically segmented SMT models. Our second task is sentiment analysis, where we resort to Arabic lemmatization to improve sentiment analysis of Arabic tweets and blog posts. We explore translation in the opposite direction, from Arabic into English in order to evaluate the loss of sentiment predictability when Arabic social media posts are translated to English, manually or using an SMT system. We use state-of-the-art Arabic and English sentiment Analysis systems and develop automatically generated Arabic lexicons from lemmatized tweets to improve this task.

Arabic language is one of the most spoken languages in the world, it’s the official language of many countries and the fourth most used language on the internet. Arabic texts are often written without diacritic marks. However, those marks are important to clarify the sense and meaning of words. Automatic diacritization is the process of assigning diacritics to letters, and it’s an important field in Arabic Natural Language Processing (ANLP). In this work, we try to find the effect of increasing the training dataset on the diacritization error rate (DER) by building a new dataset and concatenating it with the Tashkeela dataset. We trained a deep learning model based on bidirectional long short-term memory BLSTM that transcribes undiacritized sequences of Arabic letters and produces an output sequence of the same length fully diacritized. Our model shows significant results on the new dataset in terms of DER and validation loss.

Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.

Deep learning has been very successful in the past decades, especially in Computer Vision and Speech Recognition fields. It has been also used successfully in the Natural Language Processing field because of the availability of an enormous amount of online text data, such as social networks and reviews websites, which have gained a lot of popularity and success in the past years. Sentiment Analysis is one of the hottest applications of Natural Language Processing (NLP). Many researchers have done excellent work on Sentiment Analysis for English language. However, the amount of work on Sentiment Analysis for Arabic language is, in comparison, very limited due to the complexity of the Arabic language's morphology and orthography. Unlike the English language, Arabic has many different dialects which makes Sentiment Analysis for Arabic more difficult and challenging, especially when working on data collected from social networks, which is known to be unstructured and noisy. Most of the work that has been done on Sentiment Analysis of Arabic language, focused on using lexicons and basic machine learning models. In addition, most of the work has been done on small datasets because of the limited number of the available annotated datasets for Arabic language. This paper proposes state-of-the-art research for Sentiment Analysis of Arabic microblogging using new techniques, and a sophisticated Arabic text data preprocessing.

The last decade had known a great interest in Arabic Natural Language Processing (NLP) applications. This interest is due to the prominent importance of this 6th most wide-spread language in the wo...

In the era of Big Data and Data Science, Text analysis within, Natural Language Processing (NLP), suffers from the curse of high dimensionality. The use of NLP in applications such as speech processing, semantic webs, and word processing has become a main element in today’s Artificial Intelligence and Big Data Applications. A natural language parsing system must incorporate three components of natural language, namely, lexicon, morphology, and syntax. As Arabic is highly derivational, each component requires extensive exploitation of the associated linguistic characteristics. Parsing Arabic sentences still has open challenges due to several reasons including the relatively free word order of Arabic, the length of sentences, and the omission of diacritics (vowels) in written Arabic and the frequency of pro-drop phenomena. This research exploits Visual Prolog to provide a scalable platform for Arabic parser and explains the details of the used lexicon and parser and shows the scalability of the system to address more functions.

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other languages like English. The proposed model tackles Arabic Sentiment Analysis (ASA) by using a DL approach. ASA is a challenging field where Arabic language has a rich morphological structure more than other languages. In this work, Long Short-Term Memory (LSTM) as a deep neural network has been used for training the model combined with word embedding as a first hidden layer for features extracting. The results show an accuracy of about 82% is achievable using DL method.

AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP

Nowadays, sentiment analysis is one of the most crucial research fields of Natural Language Processing (NLP), and it is widely applied in a variety of applications such as marketing and politics. However, the Arabic language still lacks sufficient language resources to enable the tasks of opinion and emotion analysis comparing to other language such as English. Additionally, manual annotation requires a lot of effort and time. In this article, we address this problem and propose a novel automated annotation platform for sentiment analysis called AlgBERT by providing annotated corpus and using deep learning technology that includes many automatic natural language processing algorithms, which is the basis for text classification and opinion analysis. We suggest using BERT model as a method; it is the abbreviation of Bidirectional Encoder Representations from Transformers, as it is one of the most effective technologies in terms of results in different world languages. We used around of 54K comments collected from social networking (Twitter, YouTube) written in Arabic and Algerian dialects. Our AlgBERT system obtained excellent results with an accuracy of 91.04%, and this is considered as one of the best results for opinion analysis in Algerian dialect.

Arabic Named Entity Recognition (ANER) systems aim to identify and classify Arabic Named entities (NEs) within Arabic text. Other important tasks in Arabic Natural Language Processing (NLP) depends on ANER such as machine translation, question-answering, information extraction, etc. In general, ANER systems can be classified into three main approaches, namely, rule-based, machine-learning or hybrid systems. In this paper, we focus on research progress in machine-learning (ML) ANER and compare between linguistic resource, entity type, domain, method and performance. We also highlight the challenges when processing Arabic NEs through ML systems.

Human-computer interaction for virtual-real fusion

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

This paper discusses denotational means of lexicon dictionary by using the Associative Memory (AM), which decreased the memory location used to store different types of information. The AM flexibility retrieves the information in both sides (Arabic or English). The AM is a memory to store and retrieve the information using the associative patterns. The AM works as candidates for information and to recognize the errors caused by transmitted and wrong computed when store and retrieve the information. In other side the lexicon dictionary is supporting the artificial Intelligent Applications, especially the machine and instant translation.