A Bayesian-Network Approach to Lexical Disambiguation

lexical ambiguity can be syntactic If it involves mare than one grammatical category for a single word, or semantic If more than one meoning con be associated with a word. In thls article we discuss the application of o Boyesion-network model In the resolutlon of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatlcolly for any context-free grommar, and a subnetwork for semontlc analysis, which, In the spirit of Fillmore’s (1968) case grammars, seeks to fulfill the required cases of all condidotes for verb of the sentence. Solving for the highest /olnt probability of the variables conditloned upon the evidences to the network yields the most likely candidate with its meaning, along with Its cases and respective meanings. This Is achieved by flxlng the values of oil evidence nodes concurrently, and then performing a stochastic simulation in which the remaining nodes are updated proboblllstlcally with a high degree of porallellsm. The process of dlsomblguatlon Is directed neither by the syntax nor the semantics, but rather by the Interrelation between the two subnetworks. The use of a Bayeslan-network model allows us to express this Interrelation between the two subnetworks and among their constituents in a rather direct and rigorous way that, In connection with the convergence properties of the stochastic simulation, reveals a very robust model.