Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/533008
Title: | Development of Techniques for NER in Twitter Based on EHMM for Conversational Tamil Language |
Researcher: | Vasunthira Devi, N |
Guide(s): | Ponnusamy, R |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Mother Teresa Womens University |
Completed Date: | 2023 |
Abstract: | Natural language processing frameworks take series of words (sentences) as their input and deliver organized portrayals. The motivation behind this task is to assess NER frameworks for Tamil. In contemporary Tamil there are numerous negative indicators that cannot simply be inferred from the Tamil Negation of current managing languages. The various works have formulated different frameworks of theory to manage the Tamil negation complexities with certain restricted success level. Subject-verb agreement is required for the grammaticality of a Tamil sentence. Tamil allows subject and object drop as well as verb less sentences. Negation and Adjective based rules can help in identifying the category of the tweets for sentiment mining. The present Natural Language Processing (NLP) algorithm depend on machine adapting, particularly measurable machine learning. The worldview of machine taking in is not quite the same as that of earlier endeavours at dialect preparing. newline |
Pagination: | Xvi, 153p. |
URI: | http://hdl.handle.net/10603/533008 |
Appears in Departments: | Department of Computer Science |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 17.75 kB | Adobe PDF | View/Open |
02_certificate.pdf | 196.63 kB | Adobe PDF | View/Open | |
03_abstract.pdf | 7.89 kB | Adobe PDF | View/Open | |
04_declaration.pdf | 393.34 kB | Adobe PDF | View/Open | |
05_ackowledgement.pdf | 70.27 kB | Adobe PDF | View/Open | |
06_contents.pdf | 31.35 kB | Adobe PDF | View/Open | |
07_list of tables.pdf | 8.46 kB | Adobe PDF | View/Open | |
08_list of figures.pdf | 10.93 kB | Adobe PDF | View/Open | |
09_abbreviations.pdf | 10.88 kB | Adobe PDF | View/Open | |
10_chapter 1.pdf | 531.62 kB | Adobe PDF | View/Open | |
11_chapter 2.pdf | 169.45 kB | Adobe PDF | View/Open | |
12_chapter 3.pdf | 468.84 kB | Adobe PDF | View/Open | |
13_chapter 4.pdf | 357.97 kB | Adobe PDF | View/Open | |
14_chapter 5.pdf | 684.62 kB | Adobe PDF | View/Open | |
15_chapter 6.pdf | 79.26 kB | Adobe PDF | View/Open | |
16_bibliography.pdf | 137.22 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 32.03 kB | Adobe PDF | View/Open |
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