An automatically built named entity lexicon for Arabic

We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN’s instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from 95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold

An automatic diacritization algorithm for undiacritized Arabic text

Modern Standard Arabic (MSA) is used today in most written and some spoken media. It is, however, not the native dialect of any country. Recently, the rate of the written dialectal Arabic text increased dramatically. Most of these texts have been written in the Egyptian dialectal, as it is considered the most widely used dialect and understandable throughout the Middle East. Like other Semitic languages, in written Arabic, short vowels are not written, but are represented by diacritic marks. Nonetheless, these marks are not used in most of the modern Arabic texts (for example books and newspapers). The absence of diacritic marks creates a huge ambiguity, as the un-diacritized word may correspond to more than one correct diacritization (vowelization) form. Hence, the aim of this research is to reduce the ambiguity of the absences of diacritic marks using hybrid algorithm with significantly higher accuracy than the state-of-the-art systems for MSA. Moreover, this research is to implement and evaluate the accuracy of the algorithm for dialectal Arabic text. The design of the proposed algorithm based on two main techniques as follows: statistical n-gram along with maximum likelihood estimation and morphological analyzer. Merging the word, morpheme, and letter levels with their sub-models together into one platform in order to improve the automatic diacritization accuracy is the proposition of this research. Moreover, by utilizing the feature of the case ending diacritization, which is ignoring the diacritic mark on the last letter of the word, shows a significant error improvement. The reason for this remarkable improvement is that the Arabic language prohibits adding diacritic marks over some letters. The hybrid algorithm demonstrated a good performance of 97.9% when applied to MSA corpora (Tashkeela), 97.1% when applied on LDC’s Arabic Treebank-Part 3 v1.0 and 91.8% when applied to Egyptian dialectal corpus (CallHome). The main contribution of this research is the hybrid algorithm for automatic diacritization of undiacritized MSA text and dialectal Arabic text. The proposed algorithm applied and evaluated on Egyptian colloquial dialect, the most widely dialect understood and used throughout the Arab world, which is considered as first time based on the literature review

Morphological, syntactic and diacritics rules for automatic diacritization of Arabic sentences

AbstractThe diacritical marks of Arabic language are characters other than letters and are in the majority of cases absent from Arab writings. This paper presents a hybrid system for automatic diacritization of Arabic sentences combining linguistic rules and statistical treatments. The used approach is based on four stages. The first phase consists of a morphological analysis using the second version of the morphological analyzer Alkhalil Morpho Sys. Morphosyntactic outputs from this step are used in the second phase to eliminate invalid word transitions according to the syntactic rules. Then, the system used in the third stage is a discrete hidden Markov model and Viterbi algorithm to determine the most probable diacritized sentence. The unseen transitions in the training corpus are processed using smoothing techniques. Finally, the last step deals with words not analyzed by Alkhalil analyzer, for which we use statistical treatments based on the letters. The word error rate of our system is around 2.58% if we ignore the diacritic of the last letter of the word and around 6.28% when this diacritic is taken into account

Exploiting Arabic Diacritization for High Quality Automatic Annotation

International audienceWe present a novel technique for Arabic morphological annotation. The technique utilizes diacritization to produce morphological annotations of quality comparable to human annotators. Although Arabic text is generally written without diacritics, diacritization is already available for large corpora of Arabic text in several genres. Furthermore, diacritization can be generated at a low cost for new text as it does not require specialized training beyond what educated Arabic typists know. The basic approach is to enrich the input to a state-of-the-art Arabic morphological analyzer with word diacritics (full or partial) to enhance its performance. When applied to fully diacritized text, our approach produces annotations with an accuracy of over 97% on lemma, part-of-speech, and tokenization combined

Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.Comment: 18 pages, 17 figures, 14 table