Abstract

Named-Entity-Recognition (NER) is one of the major tasks under Natural Language Processing, which is widely used in the fields of Computer Science and Computational Linguistics. However, the amount of prior research done on NER for Sinhala is very minimal. In this paper, we present data-driven techniques to detect Named Entities in Sinhala text, with the use of Conditional Random Fields (CRF) and Maximum Entropy (ME) statistical modeling methods. Results obtained from experiments indicate that CRF, which provided the highest accuracy for the same task for other languages outperforms ME in Sinhala NER as well. Furthermore, we identify different linguistic features such as orthographic word level and contextual information that are effective with both CRF and ME Algorithms.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.