ORTHOGRAPHIC ENRICHMENT FOR ARABIC GRAMMATICAL ANALYSIS

Thesis (Ph.D.) - Indiana University, Linguistics, 2010The Arabic orthography is problematic in two ways: (1) it lacks the short vowels, and this leads to ambiguity as the same orthographic form can be pronounced in many different ways each of which can have its own grammatical category, and (2) the Arabic word may contain several units like pronouns, conjunctions, articles and prepositions without an intervening white space. These two problems lead to difficulties in the automatic processing of Arabic. The thesis proposes a pre-processing scheme that applies word segmentation and word vocalization for the purpose of grammatical analysis: part of speech tagging and parsing. The thesis examines the impact of human-produced vocalization and segmentation on the grammatical analysis of Arabic, then applies a pipeline of automatic vocalization and segmentation for the purpose of Arabic part of speech tagging. The pipeline is then used, along with the POS tags produced, for the purpose of dependency parsing, which produces grammatical relations between the words in a sentence. The study uses the memory-based algorithm for vocalization, segmentation, and part of speech tagging, and the natural language parser MaltParser for dependency parsing. The thesis represents the first approach to the processing of real-world Arabic, and has found that through the correct choice of features and algorithms, the need for pre-processing for grammatical analysis can be minimized

The Impact of Arabic Diacritization on Word Embeddings

Word embedding is used to represent words for text analysis. It plays an essential role in many Natural Language Processing (NLP) studies and has hugely contributed to the extraordinary developments in the field in the last few years. In Arabic, diacritic marks are a vital feature for the readability and understandability of the language. Current Arabic word embeddings are non-diacritized. In this paper, we aim to develop and compare word embedding models based on diacritized and non-diacritized corpora to study the impact of Arabic diacritization on word embeddings. We propose evaluating the models in four different ways: clustering of the nearest words; morphological semantic analysis; part-of-speech tagging; and semantic analysis. For a better evaluation, we took the challenge to create three new datasets from scratch for the three downstream tasks. We conducted the downstream tasks with eight machine learning algorithms and two deep learning algorithms. Experimental results show that the diacritized model exhibits a better ability to capture syntactic and semantic relations and in clustering words of similar categories. Overall, the diacritized model outperforms the non-diacritized model. Interestingly, we obtained some more interesting findings. For example, from the morphological semantics analysis, we found that with the increase in the number of target words, the advantages of the diacritized model are also more obvious, and the diacritic marks have more significance in POS tagging than in other tasks

Prediction of Part of Speech Tags for Punjabi using Support Vector Machines

Abstract: Part-of-Speech (POS

Ensemble Morphosyntactic Analyser for Classical Arabic

Classical Arabic (CA) is an influential language for Muslim lives around the world. It is the language of two sources of Islamic laws: the Quran and the Sunnah, the collection of traditions and sayings attributed to the prophet Mohammed. However, classical Arabic in general, and the Sunnah, in particular, is underexplored and under-resourced in the field of computational linguistics. This study examines the possible directions for adapting existing tools, specifically morphological analysers, designed for modern standard Arabic (MSA) to classical Arabic. Morphological analysers of CA are limited, as well as the data for evaluating them. In this study, we adapt existing analysers and create a validation data-set from the Sunnah books. Inspired by the advances in deep learning and the promising results of ensemble methods, we developed a systematic method for transferring morphological analysis that is capable of handling different labelling systems and various sequence lengths. In this study, we handpicked the best four open access MSA morphological analysers. Data generated from these analysers are evaluated before and after adaptation through the existing Quranic Corpus and the Sunnah Arabic Corpus. The findings are as follows: first, it is feasible to analyse under-resourced languages using existing comparable language resources given a small sufficient set of annotated text. Second, analysers typically generate different errors and this could be exploited. Third, an explicit alignment of sequences and the mapping of labels is not necessary to achieve comparable accuracies given a sufficient size of training dataset. Adapting existing tools is easier than creating tools from scratch. The resulting quality is dependent on training data size and number and quality of input taggers. Pipeline architecture performs less well than the End-to-End neural network architecture due to error propagation and limitation on the output format. A valuable tool and data for annotating classical Arabic is made freely available

A computational model of modern standard arabic verbal morphology based on generation

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Filosofía y Letras, Departamento de Lingüística, Lenguas Modernas, Lógica y Fª de la Ciencia y Tª de la Literatura y Literataura Comparada. Fecha de lectura: 29-01-2013The computational handling of non-concatenative morphologies is still a challenge in the field of natural language processing. Amongst the various areas of research, Arabic morphology stands out due to its highly complex structure. We propose a model for Arabic verbal morphology based on a root-and-pattern approach, which satisfies both computational consistency and an elegant formalization. Our model defines an abstract representation of prosodic templates and a set of intertwined morphemes that operate at different phonological levels, as well as a separate module of rewrite rules to deal with morphophonological and orthographic alterations. Our verbal system model asserts that Arabic exhibits two conjugational classes. The computational system, named Jabalín, is focused on generation—the program generates a full annotated lexicon of verbal forms, which is subsequently used to develop a morphological analyzer and generator. The input of the system consists of a lexicon of 15,452 verb lemmas of both Classical Arabic and Modern Standard Arabic—taken from El-Dahdah (1991)—comprising a total of 3,706 roots. The output of the system is a lexicon of 1,684,268 verbal inflected forms. We carried out an evaluation against a lexicon of inflected verbs provided by the analyzer ElixirFM (Smrž, 2007a; 2007b), which we considered a Golden Standard, achieving a precision of 99.52%. Additionally, we compared our lexicon with a list of the most frequent verb lemmas—including the most frequent verbs from each conjugation—taken from Buckwalter and Parkinson (2010). The list includes 825 verbs which are all included in our lexicon and passed an evaluation test with 99.27% of accuracy. Jabalín is available under a GNU license, and can be accessed and tested through an online interface, at http://elvira.lllf.uam.es/jabalin/, hosted at the LLI-UAM lab. The Jabalín interface provides different functionalities: analyze a form, generate the inflectional paradigm of a verb lemma, derive a root, show quantitative data, and explore the database, which includes data from the evaluation. ii Key words: Computational Linguistics, Natural Language Processing, Arabic Computational Morphology, Root-and-Pattern Morphology, Non-concatenative Morphology, Templatic Morphology, Root-and-Prosody Morphology, Computational Prosodic Morphology.Los sistemas morfológicos de tipo no concatenativo siguen siendo uno de los mayores retos para el procesamiento del lenguaje natural. Entre las diversas líneas de investigación, el estudio de la morfología del árabe destaca por ser un sistema de gran complejidad estructural. En el presente proyecto de investigación, se propone un modelo de morfología verbal del árabe basado en un enfoque root-and-pattern, así como formalmente elegante y coherente desde el punto de vista computacional. El modelo propuesto se apoya fundamentalmente en una formalización abstracta de los esquemas prosódicos y su interrelación con el material morfológico. Paralelamente, el sistema cuenta con un módulo de reglas que tratan las alteraciones morfofonológicas y ortográficas del árabe. El modelo del sistema verbal propone, y se asienta en la idea de que, existen sólo dos clases conjugacionales en árabe. El sistema computacional, llamado Jabalín, está orientado a la generación: el programa genera un lexicón de formas verbales con la información lingüística asociada. El lexicón se emplea a continuación para desarrollar un analizador y generador morfológicos. Como entrada, el sistema recibe un lexicón de lemas verbales de 15.452 entradas (tomado de El-Dahdah, 1991), que combina léxico tanto del árabe clásico como del árabe estándar moderno, y cuenta con un total de 3.706 raíces. La salida es un lexicón de 1.684.268 formas verbales flexionadas. Se ha llevado a cabo una evaluación contra un lexicón de formas verbales extraído del analizador ElixirFM (Smrž, 2007a; 2007b), con una precisión de 99,52%. Por otro lado, el lexicón se ha evaluado también contra una lista de verbos más frecuentes (incluyendo los lemas más frecuentes de cada tipo de conjugación) sacada de Buckwalter y Parkinson (2010). El total de los 825 verbos que componen la lista están incluidos en nuestro lexicón de lemas verbales y presentan una precisión del 99.27%. El sistema Jabalín, desarrollado bajo licencia GNU, cuenta además con una interfaz web donde se pueden realizar consultas en árabe, http://elvira.lllf.uam.es/jabalin/, albergada en el LLI-UAM. La interfaz cuenta iv con varias funcionalidades: analizar forma, generar flexión de un lema verbal, derivar raíz, mostrar datos cuantitativos, y explorar la base de datos, que incluye los datos de la evaluación. Palabras clave: Lingüística Computacional, Procesamiento del Lenguaje Natural, Morfología Computacional del Árabe, morfología root-and-pattern, morfología no-concatenativa, morfología templática, morfología root-and-prosody, morfología prosódica computacional

Statistical morphological disambiguation with application to disambiguation of pronunciations in Turkish /

The statistical morphological disambiguation of agglutinative languages suffers from data sparseness. In this study, we introduce the notion of distinguishing tag sets (DTS) to overcome the problem. The morphological analyses of words are modeled with DTS and the root major part-of-speech tags. The disambiguator based on the introduced representations performs the statistical morphological disambiguation of Turkish with a recall of as high as 95.69 percent. In text-to-speech systems and in developing transcriptions for acoustic speech data, the problem occurs in disambiguating the pronunciation of a token in context, so that the correct pronunciation can be produced or the transcription uses the correct set of phonemes. We apply the morphological disambiguator to this problem of pronunciation disambiguation and achieve 99.54 percent recall with 97.95 percent precision. Most text-to-speech systems perform phrase level accentuation based on content word/function word distinction. This approach seems easy and adequate for some right headed languages such as English but is not suitable for languages such as Turkish. We then use a a heuristic approach to mark up the phrase boundaries based on dependency parsing on a basis of phrase level accentuation for Turkish TTS synthesizers