Abstract

Problem statement: Named Entity Recognition (NER) is a task to identi fy proper names as well as temporal and numeric expressions, in an ope n-domain text. The NER task can help to improve the performance of various Natural Language Processing (NLP) applications such as Information Extraction (IE), Information Retrieval (IR) and Que stion Answering (QA) tasks. This study discusses on the Named Entity Recognition of Arabic (NERA). The motivation is due to the lack of resources for Arabic named entities and to enhance the accuracy t hat has been reached in previous NERA systems. Approach: This system is designed based on neural network ap proach. The main task of neural network approach is to automatically learn to recog nize component patterns and make intelligent decisions based on available data and it can also b e applied to classify new information within large databases. The use of machine learning approach to classify NER from Arabic text based on neural network technique is proposed. Neural network approach has performed successfully in many areas of artificial intelligence. The system involves three stages: the first stage is pre-processing that clea ns the collected data, the second involves converting Arab ic letters to Roman alphabets and the final stage applies neural network to classify the collected da ta. Results: The accuracy of the system is 92 %. The system is compared with decision tree using the sam e data. The results showed that the neural network approach achieved better than decision tree. Conclusion: These results prove that our technique is capable to recognize named entities of Arabic texts .

Highlights

  • The use of Named Entity Recognition (NER) concept has emerged as an important approach in natural language processing environments

  • When executing tasks related to handling massive amounts of information, NER systems could help in Information Extraction (IE), Information Retrieval (IR) and Question Answering (QA) tasks (Grover et al, 2008)

  • Step 4: Iteration: Increase the iteration p by one, go back to Step 2 and repeat the process until the selected error criterion is satisfied precision = number of correct named entries found by thesyStem number of named entries found by thesystem

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Summary

Introduction

The use of Named Entity Recognition (NER) concept has emerged as an important approach in natural language processing environments. Named Entity Recognition (NER) is synonymously well-known as NE extraction, NE detection or NE identification. It is regarded as one of the most important sub tasks in the process of Information Extraction. It is significant in the recognition and classification of defined named entities from large text, or in general context of newswires (Maynaed et al, 2008). The main goal of Named Entity Recognition (NER) task is the attempt to increase performance accuracy with regard to the identification and extraction of named entities.

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