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題名 Classification and clustering for case-based criminal summary judgments
作者 劉昭麟
Liu,Chao-Lin;Chang,Cheng-Tsung;Ho,Jim-How
貢獻者 國立政治大學資訊科學系
關鍵詞 Classification;clustering;case-based criminal summary judgments
日期 2003-06
上傳時間 27-May-2010 16:48:31 (UTC+8)
摘要 We investigate the effectiveness of machine-generated criteria for classification problems related to criminal summary judgments. Our system utilizes documents of closed lawsuits as training data for generating keyword-based and case-based classification criteria, and applies these machine-generated criteria for the classification tasks. To construct databases of the classification criteria, we employ different levels of lexical knowledge in extracting information from legal documents in Chinese, and build a case instance for each closed lawsuit. Experimental results indicate that case-based classification outperforms keyword-based classification, and that machine-generated cases may offer performance accuracy that is about 7% below that of human-provided cases. Hoping to boost inference efficiency of our classifiers, we also design methods that merge the machine-generated criteria. Empirical results show that our methods can maintain the classification quality within 20% of the quality achieved by human-provided cases, even when we aggressively reduce the number of previously machine-generated cases by about seventy percents.
關聯 Proceedings of the Ninth International Conference on Artificial Intelligence and Law
資料類型 conference
DOI http://dx.doi.org/10.1145/1047788.1047840
dc.contributor 國立政治大學資訊科學系en_US
dc.creator (作者) 劉昭麟zh_TW
dc.creator (作者) Liu,Chao-Lin;Chang,Cheng-Tsung;Ho,Jim-How-
dc.date (日期) 2003-06en_US
dc.date.accessioned 27-May-2010 16:48:31 (UTC+8)-
dc.date.available 27-May-2010 16:48:31 (UTC+8)-
dc.date.issued (上傳時間) 27-May-2010 16:48:31 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/39684-
dc.description.abstract (摘要) We investigate the effectiveness of machine-generated criteria for classification problems related to criminal summary judgments. Our system utilizes documents of closed lawsuits as training data for generating keyword-based and case-based classification criteria, and applies these machine-generated criteria for the classification tasks. To construct databases of the classification criteria, we employ different levels of lexical knowledge in extracting information from legal documents in Chinese, and build a case instance for each closed lawsuit. Experimental results indicate that case-based classification outperforms keyword-based classification, and that machine-generated cases may offer performance accuracy that is about 7% below that of human-provided cases. Hoping to boost inference efficiency of our classifiers, we also design methods that merge the machine-generated criteria. Empirical results show that our methods can maintain the classification quality within 20% of the quality achieved by human-provided cases, even when we aggressively reduce the number of previously machine-generated cases by about seventy percents.-
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Proceedings of the Ninth International Conference on Artificial Intelligence and Lawen_US
dc.subject (關鍵詞) Classification;clustering;case-based criminal summary judgmentsen_US
dc.title (題名) Classification and clustering for case-based criminal summary judgmentsen_US
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1145/1047788.1047840en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1145/1047788.1047840en_US