Abstract
The notion of stochastic lexicalized tree-adjoining grammar (SLTAG) is defined and basic algorithms for SLTAG are designed. The parameters of a SLTAG correspond to the probability of combining two structures each one associated with a word. The characteristics of SLTAG are unique and novel since it is lexically sensitive (as N-gram models or Hidden Markov Models) and yet hierarchical (as stochastic context-free grammars). An algorithm for computing the probability of a sentence generated by a SLTAG is presented. Then, an iterative algorithm for estimating the parameters of a SLTAG given a training corpus is introduced.
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