Named Entity Recognition System for Sindhi Language
This work addresses the lack of NER tools for the Sindhi language, which is incremental as it applies an existing method to a new domain.
The paper tackles Named Entity Recognition for the Sindhi language, a previously unaddressed Arabic script-based language, by developing a rule-based system that achieves 98.71% accuracy on a dataset of 936 words.
Named Entity Recognition (NER) System aims to extract the existing information into the following categories such as: Persons Name, Organization, Location, Date and Time, Term, Designation and Short forms. Now, it is considered to be important aspect for many natural languages processing (NLP) tasks such as: information retrieval system, machine translation system, information extraction system and question answering. Even at a surface level, the understanding of the named entities involved in a document gives richer analytical framework and cross referencing. It has been used for different Arabic Script-Based languages like, Arabic, Persian and Urdu but, Sindhi could not come into being yet. This paper explains the problem of NER in the framework of Sindhi Language and provides relevant solution. The system is developed to tag ten different Named Entities. We have used Ruled based approach for NER system of Sindhi Language. For the training and testing, 936 words were used and calculated performance accuracy of 98.71%.