Parsing of Myanmar sentences with function tagging

TL;DR

This paper presents a method using Naive Bayes and CFG to parse Myanmar sentences, effectively handling free-phrase order and complex morphology for accurate function tagging and parsing.

Contribution

It introduces a novel approach combining Naive Bayes and CFG tailored for Myanmar's unique linguistic features for sentence parsing.

Findings

Effective function tagging for Myanmar sentences.

Successful parsing of simple and complex sentences.

High accuracy in function tagging and parsing results.

Abstract

This paper describes the use of Naive Bayes to address the task of assigning function tags and context free grammar (CFG) to parse Myanmar sentences. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step for parsing. In the task of function tagging, we use the functional annotated corpus and tag Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information. We propose Myanmar grammar rules and apply context free grammar (CFG) to find out the parse tree of function tagged Myanmar sentences. Experiments show that our analysis achieves a good result with parsing of simple sentences and three types of complex sentences.