Acquiring causal knowledge from text using connective markers
Takashi Inui · Institutional Repositories DataBase (IRDB) · 2004
One of the bottlenecks in developing natural language understanding systems is the prohibitively high cost of building and managing a comprehensive common-sense knowledge base.In this thesis, we deal with automatic knowledge acquisition from text, specifically the acquisition of causal relations.A causal relation is the relation existing between two events such that one event causes (or enables) the other event, such as "hard rain causes flooding" or "taking a train requires buying a ticket".In previous work these relations have been classified into several types of relations based on a variety of points of view.In this work, we consider 4 types of causal relations based on agents' volitionality, as proposed in the research field of 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 a given 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, focusing our attention on Japanese complex sentences including the word ため( because), we consider the following topics: ( 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