MYANMAR ENTITY IDENTIFICATION FOR NATURAL LANGUAGE UNDERSTANDING USING BIDIRECTIONAL LONG SHORT TERM MEMORY (BiLSTM)

Abstract

Entity identification is an exacting function which has commonly appropriate broad chunk of awareness in the course of feature engineering and word list to attain great achievement.. Entity Identification (EI) is indispensable of perceptive article character from basic input and resolve the division the morphemes characterizes. This paper presents every Entities Recognition (ER) for Myanmar language using Bidirectional Long Short Term Memory (BiLSTM), eliminating the need for most feature construction. Entity contains people, location, grouping, date_time_month, numerical values, etc. Myanmar expression is still ambitious to analyze Name Entity (NE) as well as familiar conversation so it bags of geographical instruction towards noticeable items, never barrier explanation among words and none capitalization comparable other languages. Myanmar Natural Language Processing (NLP) is told to be closed growing along with has directly been excruciating to be matured. Considering that logic, Entity Identification (EI) entitled collection for Burma ER analysis is annually explained and built as composing that monograph. The elucidate EI bulk is crucial for Myanmar ER research’s improvement . For planned entity classification research, those entity titled compilation is tested all the while entire the aimed evidence for Burma ER and it will also be determined. By using BiLSTM based network architecture, the best accuracy is achieved with 83.62%. Accordingly, here task dispose of the aspect engineering development and does not demand to acquire not only expression but also territory ability.