Named Entity Recognition in Punjabi Using Hidden Markov Model
Journal Title: International Journal of Computer Science & Engineering Technology - Year 2012, Vol 3, Issue 12
Abstract
Named Entity Recognition (NER) is a task to discover the Named Entities (NEs) in a document and then categorize these NEs into diverse Named Entity classes such as Name of Person, Location, River, Organization etc. Since, huge amount of work in NER has been done in English; so, we now need to concentrate ourselves in performing NER in the Indian languages (IL). As, Punjabi is not only the Indian language but also it is the official language of Punjab, So we have developed NER based system for Punjabi. This paper discusses about NER, approaches of NER and the results achieved by us by performing NER in Punjabi using Hidden Markov Model (HMM).
Authors and Affiliations
Deepti Chopra , Nusrat Jahan , Sudha Morwal
Keywords
Related Articles
- EP ID EP125215
- DOI -
- Views 125
- Downloads 0
PCB Defect Detection Using Image Enhancement
Printed circuit board (PCB) mechanically supports and electrically connects electronic components using conductive tracks. Using PCB chances of miswriting or short circuited wiring are minimized .The location of electron...
Emblematic Fuzzy C-means Clustering for Demographic Dataset
Clustering algorithms are very useful in the field of marketing, education, and healthcare and engineering. It has several advantages such as -it deals with different type of data (text, numerical, image, categorical.),h...
A Protocol for Reducing Routing Overhead in Mobile Ad Hoc Networks
In a mobile ad hoc network (MANET), frequent link breakage occures due to mobility of nodes which leads to frequent route discoveries. This overhead of route discovery cannot be tolerated. Broadcasting is a fundamental d...
Web Image Search Reranking Using CBIR
The existing image retrieval process is based on text-based approach where the input to the search engine is given as the text. Typically, in the development of an image requisition system, semantic image retrieval relie...