Arabic Natural Language Processing Challenges And Solutions Pdf


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Recent Advances in NLP: The Case of Arabic Language

Natural language processing NLP is a subfield of linguistics , computer science , and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.

The result is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition , natural language understanding , and natural-language generation.

Natural language processing has its roots in the s. Already in , Alan Turing published an article titled " Computing Machinery and Intelligence " which proposed what is now called the Turing test as a criterion of intelligence, a task that involves the automated interpretation and generation of natural language, but at the time not articulated as a problem separate from artificial intelligence. Up to the s, most natural language processing systems were based on complex sets of hand-written rules.

Starting in the late s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power see Moore's law and the gradual lessening of the dominance of Chomskyan theories of linguistics e.

In the s, representation learning and deep neural network -style machine learning methods became widespread in natural language processing, due in part to a flurry of results showing that such techniques [7] [8] can achieve state-of-the-art results in many natural language tasks, for example in language modeling, [9] parsing, [10] [11] and many others. In the early days, many language-processing systems were designed by symbolic methods, i.

More recent systems based on machine-learning algorithms have many advantages over hand-produced rules:. Despite the popularity of machine learning in NLP research, symbolic methods are still commonly used. Since the so-called "statistical revolution" [14] [15] in the late s and mids, much natural language processing research has relied heavily on machine learning.

The machine-learning paradigm calls instead for using statistical inference to automatically learn such rules through the analysis of large corpora the plural form of corpus , is a set of documents, possibly with human or computer annotations of typical real-world examples. Many different classes of machine-learning algorithms have been applied to natural-language-processing tasks. These algorithms take as input a large set of "features" that are generated from the input data.

Increasingly, however, research has focused on statistical models , which make soft, probabilistic decisions based on attaching real-valued weights to each input feature. Such models have the advantage that they can express the relative certainty of many different possible answers rather than only one, producing more reliable results when such a model is included as a component of a larger system. Some of the earliest-used machine learning algorithms, such as decision trees , produced systems of hard if-then rules similar to existing hand-written rules.

However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical models , which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors as is very common for real-world data , and produce more reliable results when integrated into a larger system comprising multiple subtasks.

Since the neural turn, statistical methods in NLP research have been largely replaced by neural networks. However, they continue to be relevant for contexts in which statistical interpretability and transparency is required.

A major drawback of statistical methods is that they require elaborate feature engineering. Since the early s, [16] the field has thus largely abandoned statistical methods and shifted to neural networks for machine learning.

Popular techniques include the use of word embeddings to capture semantic properties of words, and an increase in end-to-end learning of a higher-level task e.

In some areas, this shift has entailed substantial changes in how NLP systems are designed, such that deep neural network-based approaches may be viewed as a new paradigm distinct from statistical natural language processing. For instance, the term neural machine translation NMT emphasizes the fact that deep learning-based approaches to machine translation directly learn sequence-to-sequence transformations, obviating the need for intermediate steps such as word alignment and language modeling that was used in statistical machine translation SMT.

Latest works tend to use non-technical structure of a given task to build proper neural network. The following is a list of some of the most commonly researched tasks in natural language processing.

Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks.

Though natural language processing tasks are closely intertwined, they can be subdivided into categories for convenience.

A coarse division is given below. Based on long-standing trends in the field, it is possible to extrapolate future directions of NLP. Most more higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP see trends among CoNLL shared tasks above.

Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses. As an example, George Lakoff offers a methodology to build Natural language processing NLP algorithms through the perspective of Cognitive science , along with the findings of Cognitive linguistics , [36] with two defining aspects:. Ties with cognitive linguistics are part of the historical heritage of NLP, but they have been less frequently addressed since the statistical turn during the s.

Nevertheless, approaches to develop cognitive models towards technically operationalizable frameworks have been pursued in the context of various frameworks, e. More recently, ideas of cognitive NLP have been revived as an approach to achieve explainability , e.

From Wikipedia, the free encyclopedia. Field of computer science and linguistics. Further information: History of natural language processing. Further information: Artificial neural network. Implementing an online help desk system based on conversational agent. France: ACM. July Proceedings of the IEEE. The creation and use of such corpora of real-world data is a fundamental part of machine-learning algorithms for natural language processing.

In addition, theoretical underpinnings of Chomskyan linguistics such as the so-called " poverty of the stimulus " argument entail that general learning algorithms, as are typically used in machine learning, cannot be successful in language processing.

As a result, the Chomskyan paradigm discouraged the application of such models to language processing. Journal of Artificial Intelligence Research. Deep Learning. MIT Press. Exploring the Limits of Language Modeling. Bibcode : arXivJ. Emnlp Bibcode : arXiv Hillsdale: Erlbaum. How the statistical revolution changes computational linguistics. Four revolutions.

Language Log, February 5, Retrieved This was an early Deep Learning tutorial at the ACL and met with both interest and at the time skepticism by most participants. Until then, neural learning was basically rejected because of its lack of statistical interpretability. Until , deep learning had evolved into the major framework of NLP. Colbert: Using bert sentence embedding for humor detection.

Advances in Neural Information Processing Systems. Lithium-Ion Batteries. Macquarie University. International Journal of Innovation, Management and Technology. Archived from the original on Oxford University Press and Dictionary. Retrieved 6 May American Federation of Teachers. Cognitive science is an interdisciplinary field of researchers from Linguistics, psychology, neuroscience, philosophy, computer science, and anthropology that seek to understand the mind.

New York Basic Books. A Cognitive Theory of Cultural Meaning. Cambridge University Press. Building an RRG computational grammar. Onomazein , 34 , Grounded compositional semantics for finding and describing images with sentences. Transactions of the Association for Computational Linguistics , 2 , Bates, M Bibcode : PNAS Natural Language Processing with Python.

O'Reilly Media. Daniel Jurafsky and James H. Martin Speech and Language Processing , 2nd edition. Pearson Prentice Hall. Mohamed Zakaria Kurdi Natural Language Processing and Computational Linguistics: speech, morphology, and syntax , Volume 1. Natural Language Processing and Computational Linguistics: semantics, discourse, and applications , Volume 2. Christopher D. Introduction to Information Retrieval.

Challenges in Arabic Natural Language Processing

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Farghaly and K. Asian Lang. Farghaly , K.

Gamification is a novel IT development that focuses on how to address the use of games and apply game design techniques in a non-gaming context. Most Natural Language Processing NLP applications use large accurately labelled data sets to achieve good performance, but these data sets are hard to obtain. The standard method to produce labelled data has been through manual labour that demands a strong engagement and a heavy financial participation. For these reasons, gamification may offer smooth possibilities for wide improvements in this field. However, gamification by itself may not yield the widely-expected results if the incentives are not strong motivators. Further, based on a previous experience, we strongly believe that even financial incentives are not enough for the participants to increase their contributions. Therefore, we could incentivise the participants, through gamification, and through their need for language acquisition.

Title: Artificial intelligence and natural language processing: the Arabic corpora in online translation software. This speaks of the disproportionate presence of on-line content of Arabic language as compared to other languages which may be due to many reasons including a lack of experts in the field of the Arabic language. This research study will investigate the impact of such Machine Translation MT software and TM tools that are widely used by the Arab community for their academic and business purposes. The study aims at finding whether it is possible to bring a paradigm shift from Arabic Localization to Arabic Globalization; hence, facilitating the usage of NLP techniques in the human interface with the computer. For this study; a few machine translation software e.


PDF | The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious.


Arabic Natural Language Processing: Challenges and Solutions

Arabic is a challenging language for the field of computational linguistics. This is due to many factors including its complex and rich morphology, its high degree of ambiguity as well as the presence of a number of dialects that vary quite widely. Arabic is also a language with important geopolitical connections. It is spoken by over million people in countries with varying degrees of prosperity and stability.

Natural language processing

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The Fourth Arabic Natural Language Processing Workshop

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Natural language processing NLP is a subfield of linguistics , computer science , and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The result is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition , natural language understanding , and natural-language generation. Natural language processing has its roots in the s.

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Gamification for Arabic Natural Language Processing: Ideas into Practice

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The Fourth Arabic Natural Language Processing Workshop

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