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https://ptsldigital.ukm.my/jspui/handle/123456789/476141Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Nazlia Omar, Dr. | |
| dc.contributor.author | Naji F. Mohammed Esmaail (P54421) | |
| dc.date.accessioned | 2023-10-06T09:13:59Z | - |
| dc.date.available | 2023-10-06T09:13:59Z | - |
| dc.date.issued | 2011-05-06 | |
| dc.identifier.other | ukmvital:74582 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476141 | - |
| dc.description | Name Entity Recognition (NER) is to identify proper names as well as temporal and numeric expressions, in an open-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 Question Answering (QA) tasks. This study focuses 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 that has been reached in previous NERA systems. This system is designed based on neural network approach. The principal task of machine learning approach is to automatically learn to recognize component patterns and make intelligent decisions based on available data, and it can also be 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 data collection, the second stage is pre-processing that tokens, manual tagging and cleans the collected data, the third involves converting Arabic letters to Roman alphabets then application of ANN classification to classify the collected data. The accuracy of the system is 92 %. The system is compared with another machine learning approach that is decision tree with the same data. The results showed that the neural network approach achieved better than decision tree. These results prove that our technique is capable to recognize named entities of Arabic texts.,Master/Sarjana | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
| dc.rights | UKM | |
| dc.subject | Arabic | |
| dc.subject | Named entity recognition | |
| dc.subject | Neural network | |
| dc.subject | Natural language processing (Computer science) | |
| dc.title | Arabic named entity recognition using neural network | |
| dc.type | theses | |
| dc.format.pages | 85 | |
| dc.identifier.callno | QA76.9.N38.E845 2011 | |
| dc.identifier.barcode | 000800 | |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_74582+Source01+Source010.PDF Restricted Access | 2 MB | Adobe PDF |  View/Open |
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