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In this study, we propose to use two corpus-driven linguistic approaches for a sense prediction study. We will concentrate on the character similarity clustering approach and the concept similarity clustering approach to predict the senses of non-assigned words by using corpora and tools, such as the Chinese Gigaword Corpus and HowNet. In this study, we will evaluate sense predictions via the sense divisions of Chinese Wordnet (CWN) and Xiandai Hanyu Cidian ( Xian Han). Using these corpora, we will determine the clusters of our four target words - chi1 "eat", wan2 "play", huan4 "change", and shao1 "burn" - in order to predict all possible senses and then evaluate them. This process will demonstrate the viability of the corpus-based approaches. [ABSTRACT FROM AUTHOR]

Searching for a pattern in an Arabic text raises various problems due to the association of vocalization characters with alphabetical letters of Arabic words. This feature causes a problem for existing searching algorithms. They either fail to find all partial matches of a pattern or they may suffer from performance degradation when they are simply modified to ignore these vocalization characters. This paper presents a new coding schema for Arabic vocalization characters that will facilitate and improve the performance of searching for vocalized and unvocalized patterns in any Arabic text (vocalized or unvocalized). This schema is based on repositioning the vocalization characters at the end of each word. We present in this paper the coding and decoding algorithms needed to support our new coding schema. In addition, we explain the modifications to the Boyer-Moore algorithm that take advantage of our improved coding schema together with the complexity analysis. [ABSTRACT FROM AUTHOR]