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Title: A hybrid approach for named entity identification and classification in Telugu documents
Researcher: Sasidhar, B
Guide(s): Vinaya Babu, A
Keywords: Computer Science
Upload Date: 22-Apr-2013
University: Acharya Nagarjuna University
Completed Date: 2012
Abstract: Natural Language Processing (NLP) is concerned with Language of a document for providing a semantic view. Languages are dynamic in nature and allow inclusions and deletions with the development of a set of well defined rules. In Natural Language processing (NLP) applications, there is a need to translate Out Of Vocabulary (OOV) words, such as, technical terms. Rules are concerned with specificity of culture as well as region. Language models gain popularity usually in non-English languages. The complex nature of morphological variations specific to Indian languages are yet to be explored with the adoption of available statistical as well as language models. Natural language understanding is concerned with process of comprehending and using Languages once the words are recognized. The objective is to specify a computational model that matches with humans in linguistic tasks such as reading, writing, listening, and speaking. To develop natural language understanding model, it is required to use knowledge from many disciplines including linguistics, psycholinguistics, philosophy, computational linguistics and so on. It is necessary to understand how language works, combine all the approaches to produce complex theories and realize such complex theories as computer programs. Testing of these programs will give a clue to which of the cases fail so that the programs can be improved. By doing this process repeatedly we can finally get to know how human language processing occurs. The term Named Entity (NE) is in current use in NLP applications. The objective of Named Entity Recognition and Classification (NERC) is to identify and classify every word/term in a document into some predefined categories like Person-name,Location-name, Organization-name, Miscellaneous-name (date, time, percentage and monetary expressions) and none-of-the above .
Pagination: 108p.
Appears in Departments:Department of Computer Science & Engineering

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01_title.pdfAttached File95.28 kBAdobe PDFView/Open
02_diclaration.pdf75.32 kBAdobe PDFView/Open
03_certificate.pdf124.02 kBAdobe PDFView/Open
04_dedication.pdf5.22 kBAdobe PDFView/Open
05_acknowledgements.pdf72.34 kBAdobe PDFView/Open
06_abstract.pdf100.17 kBAdobe PDFView/Open
07_contents.pdf62.08 kBAdobe PDFView/Open
08_list of tables.pdf52.49 kBAdobe PDFView/Open
09_list of figures.pdf50.25 kBAdobe PDFView/Open
10_publications based on the thesis.pdf93.42 kBAdobe PDFView/Open
11_chapter 1.pdf270.78 kBAdobe PDFView/Open
12_chapter 2.pdf189.69 kBAdobe PDFView/Open
13_chapter 3.pdf472.96 kBAdobe PDFView/Open
14_chapter 4.pdf270.24 kBAdobe PDFView/Open
15_chapter 5.pdf57.55 kBAdobe PDFView/Open
16_references.pdf143.4 kBAdobe PDFView/Open
17_appendix.pdf107.71 kBAdobe PDFView/Open
18_synopsis.pdf850.76 kBAdobe PDFView/Open

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