Aida Nematzadeh

A Computational Cognitive Model of Novel Word Generalization

A key challenge in vocabulary acquisition is learning which of the many possible meanings is appropriate for a word. The word generalization problem refers to how children associate a word such as dog with a meaning at the appropriate category level in a taxonomy of objects, such as Dalmatians, dogs, or animals. We present the first computational study of word generalization integrated within a word-learning model. The model simulates child and adult patterns of word generalization in a word-learning task. These patterns arise due to the interaction of type and token frequencies in the input data, an influence often observed in people’s generalization of linguistic categories.

A Cognitive Model of Semantic Network Learning

Child semantic development includes learning the meaning of words as well as the semantic relations among words. A presumed outcome of semantic development is the formation of a semantic network that reflects this knowledge. We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, which adheres to the cognitive plausibility requirements of incrementality and limited computations. We demonstrate that the semantic connections among words in addition to their context is necessary in forming a semantic network that resembles an adult’s semantic knowledge.

Child Acquisition of Multiword Verbs: A Computational Investigation

Traditional theories of grammar, as well as computational modelling of language acquisition, have focused either on aspects of word learning, or grammar learning. Work on intermediate linguistic constructions (the area between words and combinatory grammar rules) has been very limited. Although recent usage-based theories of language learning emphasize the role of multiword constructions, much remains to be explored concerning the precise computational mechanisms that underlie how children learn to identify and interpret different types of multiword lexemes. The goal of the current study is to bring in ideas from computational linguistics on the topic of identifying multiword lexemes, and to explore whether these ideas can be extended in a natural way to the domain of child language acquisition. We take a first step toward computational modelling of the acquisition of a widely-documented class of multiword verbs, such as take the train and give a kiss, that children must master early in language learning. Specifically, we show that simple statistics based on the linguistic properties of these multiword verbs are informative for identifying them in a corpus of child-directed utterances. We present preliminary experiments demonstrating that such statistics can be used within a word learning model to learn associations between meanings and sequences of words.