A computational model of learning semantic roles from child-directed language

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Semantic roles are a critical aspect of linguistic knowledge because they indicate the relations of the participants in an event to the main predicate. Experimental studies on children and adults show that both groups use associations between general semantic roles such as Agent and Theme, and grammatical positions such as Subject and Object, even in the absence of familiar verbs. Other studies suggest that semantic roles evolve over time, and might best be viewed as a collection of verb-based or general semantic properties. A usage-based account of language acquisition suggests that general roles and their association with grammatical positions can be learned from the data children are exposed to, through a process of generalisation and categorisation. In this paper, we propose a probabilistic usage-based model of semantic role learning. Our model can acquire associations between the semantic properties of the arguments of an event, and the syntactic positions that the arguments appear in. These probabilistic associations enable the model to learn general conceptions of roles, based only on exposure to individual verb usages, and without requiring explicit labelling of the roles in the input. The acquired role properties are a good intuitive match to the expected properties of various roles, and are useful in guiding comprehension in the model to the most likely interpretation in the face of ambiguity. The learned roles can also be used to select the correct meaning of a novel verb in an ambiguous situation.