接続標識に基づく文書集合からの因果関係知識獲得(知識情報インフラ)(<特集>人工知能分野における博士論文)

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A major challenge in natural language understanding is to build a comprehensive common-sense knowledge base in the efficient way possible This thesis discusses automatic knowledge acqursition from text, especrally the acquisition of causal relations We consider four types of causal relations, namely, cause, effect, precondition and means They are based on agents' volitionality, as proposed in discourse understanding The idea behind knowledge acquisition is to use resultative connective markers such as "because", "but" and "if" as linguistic cues However, there is no guarantee that such a connective marker always signals the same type of causal relation Therefore, we need to create a computational model that is able to classify samples according to the causal relation. In this work, we focus on Japanese complex sentences including the word ため(because) The following questions are asked (1) What kinds and how much causal knowledge is present in the document collection, (2) How accurately can relation instances be identified, and (3) How can acquired causal knowledge be made available to applications First, we investigated the distribution of causal relation instances in Japanese newspaper articles The main part of this investigation was conducted based on human judgments using lingnstic tests. We confirmed that it is possible to acquire causal relation instances from approximately 90 % of samples Second, we assessed how accurately we can automatically acquire causal relation instances by experiments Using a machine learning technique, we achieved 80 % recall with over 95 % precision for the cause, precondition and means relations, and 30 % recall with 90 % precision for the effect relation Furthermore, the classification results suggest that one can expect to acquire over 27,000 instances of causal relations from one year of Japanese newspaper articles Third, we applied the acquired causal knowledge to annotate words with its desirability From this investigation, it became clear that causal relation instances, at least instances of cause relations and means relations, are useful for assigning desirability of words
社団法人人工知能学会の論文
2005-01-01

接続標識に基づく文書集合からの因果関係知識獲得(知識情報インフラ)(<特集>人工知能分野における博士論文)

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