عنوان

A rules based system for named entity recognition in modern standard Arabic

شماره

TLets521502

عنوان اصلي

A rules based system for named entity recognition in modern standard Arabic

نام عام مواد

[Thesis]

نام نخستين پديدآور

Elsebai, A.

نام ناشر، پخش کننده و غيره

University of Salford

تاریخ نشرو بخش و غیره

2009

جزئيات پايان نامه و نوع درجه آن

Ph.D.

کسي که مدرک را اعطا کرده

University of Salford

امتياز متن

2009

متن يادداشت

The amount of textual information available electronically has made it difficult for many users to find and access the right information within acceptable time. Research communities in the natural language processing (NLP) field are developing tools and techniques to alleviate these problems and help users in exploiting these vast resources. These techniques include Information Retrieval (IR) and Information Extraction (IE). The work described in this thesis concerns IE and more specifically, named entity extraction in Arabic. The Arabic language is of significant interest to the NLP community mainly due to its political and economic significance, but also due to its interesting characteristics. Text usually contains all kinds of names such as person names, company names, city and country names, sports teams, chemicals and lots of other names from specific domains. These names are called Named Entities (NE) and Named Entity Recognition (NER), one of the main tasks of IE systems, seeks to locate and classify automatically these names into predefined categories. NER systems are developed for different applications and can be beneficial to other information management technologies as it can be built over an IR system or can be used as the base module of a Data Mining application. In this thesis we propose an efficient and effective framework for extracting Arabic NEs from text using a rule based approach. Our approach makes use of Arabic contextual and morphological information to extract named entities. The context is represented by means of words that are used as clues for each named entity type. Morphological information is used to detect the part of speech of each word given to the morphological analyzer. Subsequently we developed and implemented our rules in order to recognise each position of the named entity. Finally, our system implementation, evaluation metrics and experimental results are presented.

موضوع مستند نشده

Media, Digital Technology and the Creative Economy

مستند نام اشخاص تاييد نشده

Elsebai, A.

مستند نام تنالگان تاييد نشده

University of Salford

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y