文章基本信息

标题：Natural Language Understanding Tools with Low Language Resource in Building Automatic Indonesian Mind Map Generator
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作者：Ayu Purwarianti ; Athia Saelan ; Irfan Afif 等
期刊名称：International Journal on Electrical Engineering and Informatics
印刷版ISSN：2085-6830
出版年度：2013
卷号：5
期号：3
出版社：School of Electrical Engineering and Informatics
摘要：Here, we describe our work in developing Indonesian Mind Map Generator that employs several Indonesian natural language understanding tools as its main engine. The Indonesian Mind Map Generator1aims to help the user in easily making a Mind Map object. The system consists of several Indonesian natural language understanding tools such as Indonesian POS Tagger, Indonesian Syntactic Parser, and Indonesian Semantic Analyzer. The methods used for developing each of Indonesian natural language understanding tools are devised to such an extend that they are enable to alleviate the low availability of Indonesian language resources. For Indonesian POS Tagger, we employed HMM and subsequently enhanced the result by using affix tree. As for the Indonesian Syntactic Parser, we compared the performance of CYK and Earley parser, which are known as common dynamic algorithms in PCFG. The Indonesian Semantic Analyzer consists of several components such as lexical semantic attachment, reference resolution, and Semantic Analyzer itself that transforms the parse tree result into first order logic representation. In our work, instead of using a rich resource on semantic information for each vocabulary, we defined several rules for the lexical semantic attachment based on POS Tags and certain words. Finally, to develop the Mind Map generator, we used the radial drawing method to visualize the first order logic representation and we also built a Mind Map editor to allow a user in modifying the Mind Map result. To evaluate the result, we conducted the experiments for each component mentioned previously. The POS Tagger accuracy achieved 96.5%, the Syntactic Parser achieved accuracy of 47.22%, and the Semantic Analyzer achieved accuracy of 62.5%. The final result of Mind Map object was evaluated by 5 respondents. The results of evaluationshowed that, for the simple sentence, the Mind Map object can be easily understood