Level-wise Segment Parser for Semantic Role Labelling for Myanmar Text
Zin Mar Kyu, Naw Lay Wah · 2018
The semantic role labelling plays a key role to extract semantic information from language text for the purpose of information retrieval, text summarization, plagiarism test, etc. This paper presents an improved semantic role labelling approach for Myanmar Text using level-wise segmented parser. It works on parsing the arguments of a sentence from small words into combination of text chucks in the form of suitable arguments to be able to express the semantic meaning of that sentence. In labeling, different forms of segmented text phrases are considered, and it is affordable to categorize different relationship types of segmented Myanmar Text phrases such as core, non-core, etc. The proposed system is trained on roughly 70,000 words with 3000 hand-annotated semantic information for general domain. This system is tested with untrained domains in order to test efficiency of our labelling ability for semantic roles. According to the experiment results, we achieve better results than other semantic role labelers of Myanmar Text.