Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

Real-word (also known as semantic or context-sensitive) spelling error is a class of error that escapes the typical spell checker which relies on dictionary look-up. This kind of error occurs when a user types a correctly spelled word-by mistake-when another is intended, e.g., “I want a peace (piece) of cake.” Further, these errors commonly arise in text written by people with dyslexia. Real-word errors are harder to detect as we need to consider the context. In this paper, we propose a spell checker that detects and corrects real-word errors for the Arabic language. Our method avoids predefined confusion sets-a simple approach used by many works tackling this problem-which limits the list of words that can be detected and corrected. Thus, our system can detect and correct a larger set of real-word errors. For the detection phase, we employ word and stem n-gram (n = 1-3) language model along with machine learning, achieving a precision and recall of 83.5% and 99.2%, respectively. And for the correction phase we use n-gram, which results in an accuracy of 98%. Our scheme is robust, with an excellent performance even when the percentage of real-word error words is high. This makes the system suitable for handling errors in post OCR recognition of Arabic text.

Abstract Question answering systems retrieve information from documents in response to queries. Most of the questions arewho-andwhat-type questions that deal with named entities. A less common and more challenging question to deal with is thewhy-question. In this paper, we introduceLemaza(Arabic forwhy), a system for automatically answeringwhy-questions for Arabic texts. The system is composed of four main components that make use of the Rhetorical Structure Theory. To evaluateLemaza, we prepared a set ofwhy-question–answer pairs whose answer can be found in a corpus that we compiled out of Open Source Arabic Corpora.Lemazaperformed best when the stop-words were not removed. The performance measure was 72.7%, 79.2% and 78.7% for recall, precision andc@1, respectively.

A Question Answering (QA) system is concerned with building a system that automatically answer questions posed by humans in a natural language. Compared to other languages, little effort was directed towards QA systems for Arabic. Due to the difficulty of handling why -questions, most Arabic QA systems tend to ignore it. In this article, we specifically address the why -question for Arabic using two different approaches and compare their performance and the quality of their answer. The first is the baseline approach, a generic method that is used to answer all types of questions, including factoid; and for the second approach, we use Rhetorical Structure Theory (RST). We evaluate both schemes using a corpus of 700 textual documents in different genres collected from Open Source Arabic Corpora (OSAC), and a set of 100 question-answer pairs. Overall, the performance measures of recall, precision, and c@1 was 68% (all three measures) for the baseline approach, and 71%, 78%, and 77.4%, respectively, for the RST-based approach. The recently introduced extension of the accuracy, the c@1 measure, rewards unanswered questions over those wrongly answered.

Abstract In Modern Standard Arabic texts are typically written without diacritical markings. The diacritics are important to clarify the sense and meaning of words. Lack of these markings may lead to ambiguity even for the natives. Often the natives successfully disambiguate the meaning through the context; however, many Arabic applications, such as machine translation, text-to-speech, and information retrieval, are vulnerable due to lack of diacritics. The process of automatically restoring diacritical marks is called diacritization or diacritic restoration. In this paper we discuss the properties of the Arabic language and the issues that are related to the lack of the diacritical marking. It will be followed by a survey of the recent algorithms that were developed to solve the diacritization problem. We also look into the future trend for researchers working in this area.

R. Morris (see IEEE Trans. Comput., vol.TC-20, p.1578-9, 1971), suggested adding an extra field to the fixed floating point system, so that exponents can be stored more efficiently. The exponents are stored in the smallest possible space, passing the extra bits to the mantissa. The extra field is used to monitor the current length of the exponent. The gain in precision and/or exponent range outweighs the overhead of the extra field and the processing speed. The authors provide implementation details, error analysis, and some future research ideas. Simulation results are provided for comparison purposes.>

A new computer simulation for a typist is described. This is unique in the sense that it is based on decisions made by the typist during the look-ahead process, takes care of hand overlapping during typing, and handles simultaneously the shifted and unshifted characters. The simulation is applied to several layouts (two of which are suggested by the authors). It was found that the reassignment of characters to keys on the QWERTY layout yields insignificant tangible improvement on typing productivity in terms of gross words per minute. However, regarding its effect on the distance travelled by the typist's fingers and the number of motions, considerable tangible improvement on typing process is realizable in terms of ease of operation and possibly less hand fatigue.