When Meaning Is Not Enough: Distributional and Semantic Cues to Word Categorization in Child Directed Speech

Distributional Lattices as a Model for Discovering Syntactic Categories in Child-Directed Speech

Distribution information plays an important role in word categorization. In this paper, we present a novel distributional model, distributional lattices to discover syntactic categories in child directed speech. A distributional lattice is a hierarchy formed by closed sets of words that are distributionally similar. Such a hierarchy is potentially useful for capturing syntactic categories by clustering words with associate patterns they occur in. In order to empirically support the suggestion that the distributional lattice is effective at categorizing words, we present a distributional lattice analysis of the Brent corpus of child-directed speech. The results show that distributional lattices are able to yield extremely accurate syntactic categories.

A neural network architecture for learning word-referent associations in multiple contexts

This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence collected from the literature of Psycholinguistics and Neurolinguistics. The multi-layered architecture takes as input raw images of objects (referents) and streams of word's phonemes (labels), builds an adequate representation, recognizes the current context, and associates label with referents incrementally, by employing a Self-Organizing Map which creates new association nodes (prototypes) as required, adjusts the existing prototypes to better represent the input stimuli and removes prototypes that become obsolete/unused. The model takes into account the current context to retrieve the correct meaning of words with multiple meanings. Simulations show that the model can reach up to 78% of word-referent association accuracy in ambiguous situations and approximates well the learning rates of humans as reported by three different authors in five Cross-Situational Word Learning experiments, also displaying similar learning patterns in the different learning conditions.