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

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.