Academic literature on the topic 'Arabic Natural Language Processing'

This thesis explores a range of approaches to the task of recognising textual entailment (RTE), i.e. determining whether one text snippet entails another, for Arabic, where we are faced with an exceptional level of lexical and structural ambiguity. To the best of our knowledge, this is the first attempt to carry out this task for Arabic. Tree edit distance (TED) has been widely used as a component of natural language processing (NLP) systems that attempt to achieve the goal above, with the distance between pairs of dependency trees being taken as a measure of the likelihood that one entails the other. Such a technique relies on having accurate linguistic analyses. Obtaining such analyses for Arabic is notoriously difficult. To overcome these problems we have investigated strategies for improving tagging and parsing depending on system combination techniques. These strategies lead to substantially better performance than any of the contributing tools. We describe also a semi-automatic technique for creating a first dataset for RTE for Arabic using an extension of the ‘headline-lead paragraph’ technique because there are, again to the best of our knowledge, no such datasets available. We sketch the difficulties inherent in volunteer annotators-based judgment, and describe a regime to ameliorate some of these. The major contribution of this thesis is the introduction of two ways of improving the standard TED: (i) we present a novel approach, extended TED (ETED), for extending the standard TED algorithm for calculating the distance between two trees by allowing operations to apply to subtrees, rather than just to single nodes. This leads to useful improvements over the performance of the standard TED for determining entailment. The key here is that subtrees tend to correspond to single information units. By treating operations on subtrees as less costly than the corresponding set of individual node operations, ETED concentrates on entire information units, which are a more appropriate granularity than individual words for considering entailment relations; and (ii) we use the artificial bee colony (ABC) algorithm to automatically estimate the cost of edit operations for single nodes and subtrees and to determine thresholds, since assigning an appropriate cost to each edit operation manually can become a tricky task.The current findings are encouraging. These extensions can substantially affect the F-score and accuracy and achieve a better RTE model when compared with a number of string-based algorithms and the standard TED approaches. The relative performance of the standard techniques on our Arabic test set replicates the results reported for these techniques for English test sets. We have also applied ETED with ABC to the English RTE2 test set, where it again outperforms the standard TED.

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology.

Arabic news articles in heterogeneous electronic collections are difficult for users to work with. Two problems are: that they are not categorized in a way that would aid browsing, and that there are no summaries or detailed metadata records that could be easier to work with than full articles. To address the first problem, schema mapping techniques were adapted to construct a simple taxonomy for Arabic news stories that is compatible with the subject codes of the International Press Telecommunications Council. So that each article would be labeled with the proper taxonomy category, automatic classification methods were researched, to identify the most appropriate. Experiments showed that the best features to use in classification resulted from a new tailored stemming approach (i.e., a new Arabic light stemmer called P-Stemmer). When coupled with binary classification using SVM, the newly developed approach proved to be superior to state-of-the-art techniques. To address the second problem, i.e., summarization, preliminary work was done with English corpora. This was in the context of a new Problem Based Learning (PBL) course wherein students produced template summaries of big text collections. The techniques used in the course were extended to work with Arabic news. Due to the lack of high quality tools for Named Entity Recognition (NER) and topic identification for Arabic, two new tools were constructed: RenA for Arabic NER, and ALDA for Arabic topic extraction tool (using the Latent Dirichlet Algorithm). Controlled experiments with each of RenA and ALDA, involving Arabic speakers and a randomly selected corpus of 1000 Qatari news articles, showed the tools produced very good results (i.e., names, organizations, locations, and topics). Then the categorization, NER, topic identification, and additional information extraction techniques were combined to produce approximately 120,000 summaries for Qatari news articles, which are searchable, along with the articles, using LucidWorks Fusion, which builds upon Solr software. Evaluation of the summaries showed high ratings based on the 1000-article test corpus. Contributions of this research with Arabic news articles thus include a new: test corpus, taxonomy, light stemmer, classification approach, NER tool, topic identification tool, and template-based summarizer – all shown through experimentation to be highly effective.<br>Ph. D.

En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimiento que a~nade al texto. En la literatura existen diversos trabajos que investigan la tarea de REN para un idioma especifico o desde una perspectiva independiente del lenguaje. Sin embargo, hasta el momento, se han publicado muy pocos trabajos que estudien dicha tarea para el arabe. El arabe tiene una ortografia especial y una morfologia compleja, estos aspectos aportan nuevos desafios para la investigacion en la tarea de REN. Una investigacion completa del REN para elarabe no solo aportaria las tecnicas necesarias para conseguir un alto rendimiento, sino que tambien proporcionara un analisis de los errores y una discusion sobre los resultados que benefician a la comunidad de investigadores del REN. El objetivo principal de esta tesis es satisfacer esa necesidad. Para ello hemos: 1. Elaborado un estudio de los diferentes aspectos del arabe relacionados con dicha tarea; 2. Analizado el estado del arte del REN; 3. Llevado a cabo una comparativa de los resultados obtenidos por diferentes tecnicas de aprendizaje automatico; 4. Desarrollado un metodo basado en la combinacion de diferentes clasificadores, donde cada clasificador trata con una sola clase de entidades nombradas y emplea el conjunto de caracteristicas y la tecnica de aprendizaje automatico mas adecuados para la clase de entidades nombradas en cuestion. Nuestros experimentos han sido evaluados sobre nueve conjuntos de test.<br>Benajiba, Y. (2009). Arabic named entity recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8318<br>Palancia

Current research in Natural Language Processing (NLP) tends to exploit corpus resources as a way of overcoming the problem of knowledge acquisition. Statistical analysis of corpora can reveal trends and probabilities of occurrence, which have proved to be helpful in various ways. Machine Translation (MT) is no exception to this trend. Many MT researchers have attempted to extract knowledge from parallel bilingual corpora. The MT problem is generally decomposed into two sub-problems: lexical selection and reordering of the selected words. This research addresses the problem of lexical selection of open-class lexical items in the framework of MT. The work reported in this thesis investigates different methodologies to handle this problem, using a corpus-based approach. The current framework can be applied to any language pair, but we focus on Arabic and English. This is because Arabic words are hugely ambiguous and thus pose a challenge for the current task of lexical selection. We use a challenging Arabic-English parallel corpus, containing many long passages with no punctuation marks to denote sentence boundaries. This points to the robustness of the adopted approach. In our attempt to extract lexical equivalents from the parallel corpus we focus on the co-occurrence relations between words. The current framework adopts a lexicon-free approach towards the selection of lexical equivalents. This has the double advantage of investigating the effectiveness of different techniques without being distracted by the properties of the lexicon and at the same time saving much time and effort, since constructing a lexicon is time-consuming and labour-intensive. Thus, we use as little, if any, hand-coded information as possible. The accuracy score could be improved by adding hand-coded information. The point of the work reported here is to see how well one can do without any such manual intervention. With this goal in mind, we carry out a number of preprocessing steps in our framework. First, we build a lexicon-free Part-of-Speech (POS) tagger for Arabic. This POS tagger uses a combination of rule-based, transformation-based learning (TBL) and probabilistic techniques. Similarly, we use a lexicon-free POS tagger for English. We use the two POS taggers to tag the bi-texts. Second, we develop lexicon-free shallow parsers for Arabic and English. The two parsers are then used to label the parallel corpus with dependency relations (DRs) for some critical constructions. Third, we develop stemmers for Arabic and English, adopting the same knowledge -free approach. These preprocessing steps pave the way for the main system (or proposer) whose task is to extract translational equivalents from the parallel corpus. The framework starts with automatically extracting a bilingual lexicon using unsupervised statistical techniques which exploit the notion of co-occurrence patterns in the parallel corpus. We then choose the target word that has the highest frequency of occurrence from among a number of translational candidates in the extracted lexicon in order to aid the selection of the contextually correct translational equivalent. These experiments are carried out on either raw or POS-tagged texts. Having labelled the bi-texts with DRs, we use them to extract a number of translation seeds to start a number of bootstrapping techniques to improve the proposer. These seeds are used as anchor points to resegment the parallel corpus and start the selection process once again. The final F-score for the selection process is 0.701. We have also written an algorithm for detecting ambiguous words in a translation lexicon and obtained a precision score of 0.89.

The term equivalence describes the relationship between a translation and the text from which it is translated. Translation is generally viewed as indeterminate insofar as there is no single acceptable translation - but many. Despite this, the rationalist metaphor of translation equivalence prevails. Rationalist approaches view translation as a process in which an original text is analysed to a level of abstraction, then transferred into a second representation from which a translation is generated. At the deepest level of abstraction, representations for analysis and generation are identical and transfer becomes redundant, while at the surface level it is said that surface textual features are transferred directly. Such approaches do not provide a principled explanation of how or why abstraction takes place in translation. They also fail to resolve the dilemma of specifying the depth of transfer appropriate for a given translation task. By focusing on the translator�s role as mediator of communication, equivalence can be understood as the coordination of information about situations and states of mind. A fundamental opposition is posited between the transfer of rule-like or codifiable aspects of equivalence and those non-codifiable aspects in which salient information is coordinated. The Translation Salience model proposes that Transfer and Salience constitute bipolar extremes of a continuum. The model offers a principled account of the translator�s interlingual attunement to multi-placed coordination, proposing that salient information can be accounted for with three primary notions: markedness, implicitness and localness. Chapter Two develops the Translation Salience model. The model is supported with empirical evidence from published translations of Arabic and English texts. Salience is illustrated in Chapter Three through contextualized interpretations associated with various Arabic communication resources (repetition, code switching, agreement, address in relative clauses, and the disambiguation of presentative structures). Measurability of the model is addressed in Chapter Four with reference to emerging computational techniques. Further research is suggested in connection with theme and focus, text type, cohesion and collocation relations.

Pre-processing Arabic text for Text-to-Speech (TTS) systems poses major challenges, as Arabic omits short vowels in writing. This omission leads to a large number of homographs, and means that Arabic text needs to be diacritized to disambiguate these homographs, in order to be matched up with the intended pronunciation. Diacritizing Arabic has generally been achieved by using rule-based, statistical, or hybrid methods that combine rule-based and statistical methods. Recently, diacritization methods involving deep learning have shown promise in reducing error rates. These deep-learning methods are not yet commonly used in TTS engines, however. To examine neural diacritization methods for use in TTS engines, we normalized and pre-processed a version of the Tashkeela corpus, a large diacritized corpus containing largely Classical Arabic texts, for TTS purposes. We then trained and tested three state-of-the-art Recurrent-Neural-Network-based models on this data set. Additionally we tested these models on the Wiki News corpus, a test set that contains Modern Standard Arabic (MSA) news articles and thus more closely resembles most TTS queries. The models were evaluated by comparing the Diacritic Error Rate (DER) and Word Error Rate (WER) achieved for each data set to one another and to the DER and WER reported in the original papers. Moreover, the per-diacritic accuracy was examined, and a manual evaluation was performed. For the Tashkeela corpus, all models achieved a lower DER and WER than reported in the original papers. This was largely the result of using more training data in addition to the TTS pre-processing steps that were performed on the data. For the Wiki News corpus, the error rates were higher, largely due to the domain gap between the data sets. We found that for both data sets the models overfit on common patterns and the most common diacritic. For the Wiki News corpus the models struggled with Named Entities and loanwords. Purely neural models generally outperformed the model that combined deep learning with rule-based and statistical corrections. These findings highlight the usability of deep learning methods for Arabic diacritization in TTS engines as well as the need for diacritized corpora that are more representative of Modern Standard Arabic.

A une époque où l’informatique a envahi tous les aspects de notre vie quotidienne, il est tout à fait normal de voir le domaine informatique participer aux travaux en sciences humaines et sociales, et notamment en linguistique où le besoin de développer des logiciels informatiques se fait de plus en plus pressant avec le volume grandissant des corpus traités. D’où notre travail de thèse qui consiste en l’élaboration d’un programme EPL qui étudie le parler arabe libanais blanc. En partant d’un corpus élaboré à partir de deux émissions télévisées enregistrées puis transcrites en lettres arabes, ce programme, élaboré avec le logiciel Access, nous a permis d’extraire les mots et les collocations et de procéder à une analyse linguistique aux niveaux lexical, phonétique, syntaxique et collocationnel. Le fonctionnement de l’EPL ainsi que le code de son développement sont décrits en détails dans une partie informatique à part. Des annexes de taille closent la thèse et rassemblent le produit des travaux de toute une équipe de chercheures venant de maintes spécialités<br>At a time when computer science has invaded all aspects of our daily life, it is natural to see the computer field participating in human and social sciences work, and more particularly in linguistics where the need to develop computer software is becoming more and more pressing with the growing volume of analyzed corpora. Hence our thesis which consists in elaborating a program EPL that studies the white Lebanese Arabic speech. Starting from a corpus elaborated from two TV programs recorded then transcribed in Arabic letters, the program EPL, developed with Access software, allowed us to extract words and collocations, and to carry out a linguistic analysis on the lexical, phonetic, syntactic and collocational levels. The EPL’s functioning as well as its development code are described in the computer part. Important annexes conclude the thesis and gather the result of the work of a team of researchers coming from different specialties