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Among dependency parsing algorithms available in MALTParser and MSTParser, the best accuracy for parsing Indonesian language is achieved by Chu-Liu-Edmonds algorithm. This is due to the long distance relation between head and dependent in Indonesian sentences. Most of inaccuracy parsing results is caused by the non-verb sentence root score where there are many cases in Indonesian sentence having a...
Despite the fact that study of statistical machine translation has been growing rapidly to date, there has not been much research done about Indonesian-Japanese statistical machine translation. The previous research about Indonesian-Japanese statistical machine translation has shown several problems in translation process, such as low coverage corpus data, unknown words, and sentence reordering problem...
This paper presents our research in building an error analysis system that is used to help the language learning process, especially in writing a Japanese sentence with correct grammar. The grammar of the Japanese input is evaluated by the system using several natural language processing (NLP) tools. Type of grammatical errors that can be analysed by the system are writing error, particle usage error,...
Text classification is a useful task in text mining. Most researchers employ one word weight type in the text classification. Here, we proposed to build a keyword list by combining several word weights for a rule based multi label text classification. Through this research, we conducted experiments on the term distribution clustering to produce the best automatic generated keyword list. We compared...
Dependency parsing has gained many focus lately for its many advantages over constituency-based. Dependency parsing uses dependency grammar. The observation which drives dependency grammar is a simple one: In a sentence, all but one word depends on other words. The one word that does not depend on any other is called the root of the sentence. A word depends on another either if it is a complement...
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