Abstract
In this paper we present a statistical transliteration technique that is language independent. This technique uses statistical alignment models and Conditional Random Fields (CRF). Statistical alignment models maximizes the probability of the observed (source, target) word pairs using the expectation maximization algorithm and then the character level alignments are set to maximum posterior predictions of the model. CRF has efficient training and decoding processes which is conditioned on both source and target languages and produces globally optimal solution.
Cite
CITATION STYLE
Shishtla, P., Surya Ganesh, V., Subramaniam, S., & Varma, V. (2009). A language-independent transliteration schema using character aligned models at NEWS 2009. In NEWS 2009 - 2009 Named Entities Workshop: Shared Task on Transliteration at the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009 (pp. 40–43). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1699705.1699715
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
