dc.contributor.author | Kituku, Benson N | |
dc.date.accessioned | 2013-03-01T12:26:57Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Masters of science in computer science | en |
dc.identifier.uri | http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/13060 | |
dc.description.abstract | There has been exponential multiplication of electronic information for the last two
decades which has generated a large digital library for everyone to access over the
internet. However, this library consists of unstructured documents where queries cannot
be run as with a database so as to get preview of the content or certain details of interest.
As a result, a need for language tool arises.
Natural language processing has provided a channel whereby the above challenge can be
resolved using Name entity recognition (NER) in which a machine learning system is
developed which can identify organization, personal and location names in various
documents and report them from which you can get a glimpse of the contents of the
documents.
In this project we present a Kikamba Name Entity Recognition using a memory based
approach where supervised and bootstraps learning methods are applied to a carefully
annotated corpus. To build the training set, a corpus is manually annotated. An annotated
seed is also provided to facilitate bootstrap. Simultaneously, generation of Part of Speech
tagging is done. The resultant classifiers are evaluated. The Aim of the project is a tool
for analysis of electronic documents and at the same time find out the challenges that are
peculiar to Kikamba language so as to compare with other languages which already have
been tackled. | en |
dc.description.sponsorship | Nairobi of Nairobi | en |
dc.language.iso | en | en |
dc.publisher | University of Nairobi | en |
dc.subject | Name | en |
dc.subject | entity recognition | en |
dc.subject | part of speech | en |
dc.subject | tagging | en |
dc.subject | case study | en |
dc.subject | Kikamba | en |
dc.title | Name entity recognition and part of speech tagging: case study of Kikamba | en |
dc.type | Thesis | en |
local.publisher | School of Computing and Informatics | en |