Acquiring Bayesian Networks from Text

Causal inference is one of the most fundamental reasoning processes and one that is essential for question-answering as well as more general AI applications such as decision-making and diagnosis. Bayesian Networks are a popular formalism for encoding (probabilistic) causal knowledge that allows for inference. We developed a system for acquiring causal knowledge from text. Our system identifies sentences that specify causal relations and extracts from them causal patterns, taking into account connectives such as conjunction, disjunction and negation, and recognising causes and effects by analysing terms. The dependencies among the causes and effects found in text can be encoded as Bayesian networks. We evaluated our work by comparing the network structures obtained by our system with the ones created by a human evaluator. Introduction and Motivations Causal inference is one of the most fundamental reasoning processes (Glymour, 2003; Pazzani, 1991; Trabasso’s paper in Goldman et al, 1999) and one which is essential for question-answering as well as more general AI applications such as decision-making and diagnosis. Methods for acquiring knowledge about causal rules are a prerequisite for the development of systems capable of causal inference in these applications, especially in complex domains (Girju, 2003; Kontos et al, 2002). Bayesian Networks (Pearl, 1998) are a popular formalism for encoding probabilistic causal knowledge and for causal inference. Such networks are typically acquired from data (Mani & Cooper, 2001), but text is a rich source of information about causal relations that can be exploited, even though there are a number of problems to take into account (Hearst, 1999). In this paper we discuss domain-independent methods for acquiring from text causal knowledge encoded as Bayesian networks. Background Bayesian Networks A Bayesian network (Pearl, 2000) is a directed acyclic graph whose arcs denote a direct causal influence between parent nodes (causes) and children nodes (effects). The nodes can be used to encode any random variable. For example, a person can be ill or well; the car engine can be working normally or having problems, etc. Such graph is associated with a probability distribution that satisfies the Markov Assumption. By using Bayesian networks it is possible to handle incomplete knowledge as well as to make predictions by using the conditional probability distribution tables (CPT). There is one table for each node, which describes the conditional probability of that node given the different values of its parents (Friedman & Goldszmidt, 1996). A disadvantage of these tables is that they can be huge because the size of the table is locally exponential to the number of parents of the node. The complete joint probability distribution for the network is expressed by the CPTs for all the variables together with the conditional independences described by the network (Mitchell, 1997). Identifying Causal Relations Acquiring causal knowledge from text requires, first of all, identifying portions of text that specify a causal relation (henceforth causal patterns) between causes and effects (henceforth events) such as: “Corruption and insecurity cause social problems”, “Disease provokes pain or death”, “Earthquake generates victims” (Girju & Moldovan, 2002; Wolff et al, 2002); and second, analysing these causal patterns (a) taking into account the possible presence of connectives such as conjunction, disjunction and negation and (b) identifying causes and effects by analysing terms. These analysis steps are seldom discussed in the literature and have been the focus of our research. We consider each step in turn. Finding causal patterns Causal patterns can be expressed by cues such as connectives, as in “the manager fired John because he was lazy”; verbs, as in “smoking causes cancer”; or NPs, as in “Viruses are the cause of neurological diseases“. After a preliminary analysis, we decided to concentrate in this first stage on causal patterns in which both events are expressed as noun phrases (ignoring cases such as in “the manager fired John because he was lazy”). We also decided to restrict the number of cues to the cause words in Roget Thesaurus found to be the most frequent in texts using Google, together with the causal verbs proposed by Girju and Moldovan (2002). Girju and Moldovan focused on explicit intra-sentential syntactic patterns of the forms and . In the latter they use WordNet (Fellbaum, 1998) causal relations to find noun concepts of the verbs with nominalizations. They developed a method for automatic detection of causation patterns and semi-automatic validation of ambiguous lexico-syntactic patterns that refer to causal relationships. In this work we used the causal verbs that