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Part-Of-Speech (POS) Tagging is the process of assigning the words with their categories that best suits the definition of the word as well as the context of the sentence in which it is used. In this paper, we describe a machine learning algorithm for Myanmar Tagging using a corpus-based approach. In order to tag Myanmar language, we need to take part word segmentation, part of speech tagging using HMM and several Tag-sets. Thus, this paper deals with a combination of supervised and un-supervised learning which use pre-tagged and untagged corpus respectively. To assign to each word with the correct tag, we describe Supervised POS Tagging by using the class labels in terms of predictor features on manually tagged corpus and also describe Unsupervised POS Tagging for automatically training without using a manually tagged corpus. By experiments, the best configuration is investigated on different amount of training data and the accuracy is 97.56%.

At present, there is a vast amount of duplicated data or redundant data in storage systems. Data de-duplication can eliminate multiple copies of the same file and duplicated segments or chunks of data within those files. In these days, therefore, data de-duplication becomes an interesting field in storage environments especially in persistent data storage for data centers. Many data deduplication mechanisms have been proposed for efficient data deduplication in order to safe storage space. Current issue for data deduplication is to avoid full-chunk indexing to identify the incoming data is new, which is time consuming process. In this paper, we propose an efficient indexing mechanism for this problem using the advantage of B+ tree properties. In our proposed system, we will first separate the file into variable-length chunks using Two Thresholds Two Divisors chunking algorithm. ChunkIDs are then obtained by applying hash function to the chunks. The resulted ChunkIDs are used to build as indexing keys in B+ tree like index structure. So the searching time for the duplicate file chunks reduces from O (n) to O (log n), which can avoid the risk of full chunk indexing.