2010 43rd Hawaii International Conference on System Sciences 2010
DOI: 10.1109/hicss.2010.97
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Causal Network Construction to Support Understanding of News

Abstract: To support understanding of news, we propose a novel TEC model (Topic-Event Causal relation model) and describe the method to construct a Causal Network in the TEC model. The model includes two types of keywords to represent casual relations: topic keywords, which describe topics, and event keywords, which describe events. In the TEC model, causal relations are represented by an edgelabeled directed graph. A source vertex represents the cause of an event, and a destination vertex represents the result of that… Show more

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Cited by 12 publications
(20 citation statements)
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“…Choudhary et al [26] propose a set of transformations to capture the evolution of an actor and interactions among actors. Ishii et al [27] classify extracted sentences to define some simple language patterns in Japanese so as to extract causal relations, but their work cannot handle cases that are not defined in their patterns.…”
Section: News Event Relationship Analysismentioning
confidence: 99%
“…Choudhary et al [26] propose a set of transformations to capture the evolution of an actor and interactions among actors. Ishii et al [27] classify extracted sentences to define some simple language patterns in Japanese so as to extract causal relations, but their work cannot handle cases that are not defined in their patterns.…”
Section: News Event Relationship Analysismentioning
confidence: 99%
“…Sakaji et al [16] proposed a method for extracting causality from unstructured text using clue phrases such as "tame: because" and "niyori: due to". Then, Ishii et al [17] constructed causal network using clue phrases to support understanding of news. We first tried to extract causality using clue phrases.…”
Section: Crowdsourcing and Nlp For Linked Datamentioning
confidence: 99%
“…Other works produced in the last decade include the development of a corpus to identify patterns that are both temporal and causal [40], ontology enrichment using causation relations [41], and automatic detection of causal relations in Arabic language [23]. Some application-specific papers consider the extraction of causal networks from news topics [7], [42], [43], and finding causality in the specialized domain of environment [11]. We will not discuss these here due to brevity of space.…”
Section: B the New Millenium: Focus On Large And Domain Independent T...mentioning
confidence: 99%