Mahmoud Abd El Hafez M. Adam

Name identification has been worked on quite intensively for the past few years, and has been incorporated into several products revolving around natural language processing tasks. Many researchers have attacked the name identification problem in a variety of languages, but only a few limited research efforts have focused on named entity recognition for Arabic script. This is due to the lack of resources for Arabic named entities and the limited amount of progress made in Arabic natural language processing in general. In this article, we present the results of our attempt at the recognition and extraction of the 10 most important categories of named entities in Arabic script: the person name, location, company, date, time, price, measurement, phone number, ISBN, and file name. We developed the system Named Entity Recognition for Arabic (NERA) using a rule-based approach. The resources created are: a Whitelist representing a dictionary of names, and a grammar, in the form of regular expressions, which are responsible for recognizing the named entities. A filtration mechanism is used that serves two different purposes: (a) revision of the results from a named entity extractor by using metadata, in terms of a Blacklist or rejecter, about ill-formed named entities and (b) disambiguation of identical or overlapping textual matches returned by different name entity extractors to get the correct choice. In NERA, we addressed major challenges posed by NER in the Arabic language arising due to the complexity of the language, peculiarities in the Arabic orthographic system, non-standardization of the written text, ambiguity, and lack of resources. NERA has been effectively evaluated using our own tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.}

The coupling capacitor voltage transformers transient response during faults can cause protective relay mal-operation or even prevent tripping. This paper presents the CCVT transient response errors and the use of artificial neural network (ANN) to correct the CCVT secondary waveform distortion. In this paper, an ANN program is developed to recover the primary voltage from the distorted secondary voltage. The ANN is trained to achieve the inverse transfer function of the coupling capacitor voltage transformer (CCVT), which provides a good estimate of the true primary voltage from the distorted secondary voltage. The neural network is developed and trained using MATLAB simulations. The accuracy of the simulation program is
confirmed by comparison of its response with that of the target value from the simulation data.

n/a

n/a

n/a