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

towards an optimal solution to lemmatization in arabic

Abstract Lemmatization—computing the canonical forms of words in running text—is an important component in any NLP system and a key preprocessing step for most applications that rely on natural language understanding. In the case of Arabic, lemmatization is a complex task because of the rich morphology, agglutinative aspects, and lexical ambiguity due to the absence of short vowels in writing. In this paper, we introduce a new lemmatizer tool that combines a machine-learning-based approach with a lemmatization dictionary, the latter providing increased accuracy, robustness, and flexibility to the former. Our evaluations yield a performance of over 98% for the entire lemmatization pipeline. The lemmatizer tools are freely downloadable for private and research purposes

Arabic diacritization using weighted finite-state transducers

Arabic is usually written without short vowels and additional diacritics, which are nevertheless important for several applications. We present a novel algorithm for restoring these symbols, using a cascade of probabilistic finite- state transducers trained on the Arabic treebank, integrating a word-based language model, a letter-based language model, and an extremely simple morphological model. This combination of probabilistic methods and simple linguistic information yields high levels of accuracy.Engineering and Applied Science

A Semi-automatic and Low Cost Approach to Build Scalable Lemma-based Lexical Resources for Arabic Verbs

International audienceThis work presents a method that enables Arabic NLP community to build scalable lexical resources. The proposed method is low cost and efficient in time in addition to its scalability and extendibility. The latter is reflected in the ability for the method to be incremental in both aspects, processing resources and generating lexicons. Using a corpus; firstly, tokens are drawn from the corpus and lemmatized. Secondly, finite state transducers (FSTs) are generated semi-automatically. Finally, FSTsare used to produce all possible inflected verb forms with their full morphological features. Among the algorithm’s strength is its ability to generate transducers having 184 transitions, which is very cumbersome, if manually designed. The second strength is a new inflection scheme of Arabic verbs; this increases the efficiency of FST generation algorithm. The experimentation uses a representative corpus of Modern Standard Arabic. The number of semi-automatically generated transducers is 171. The resulting open lexical resources coverage is high. Our resources cover more than 70% Arabic verbs. The built resources contain 16,855 verb lemmas and 11,080,355 fully, partially and not vocalized verbal inflected forms. All these resources are being made public and currently used as an open package in the Unitex framework available under the LGPL license