Novel words in novel contexts: The role of distributional information in formclass category learning

Novel Words in Novel Contexts: The Role of Distributional Information in Form-class Category Learning Patricia A. Reeder (preeder@bcs.rochester.edu) Elissa L. Newport (newport@bcs.rochester.edu) Richard N. Aslin (aslin@cvs.rochester.edu) Department of Brain & Cognitive Sciences, University of Rochester Meliora Hall, Box 270268 Rochester, NY 14627 USA Abstract One major aspect of successful language acquisition is the ability to organize words into form class categories and generalize from properties of experienced items to novel items. Furthermore, learners must often determine how to use a new word, when there is very sparse information regarding its acceptable contexts. In this work we employ an artificial language learning paradigm to explore how adult learners, under circumstances of varying distributional cues to category boundaries, apply their knowledge of category properties to a new word. We find that in cases of strong category cues and strong category learning, adults readily generalize all of the distributional properties of the learned category to a word that shares just one context with the other category members. However, as the distributional cues regarding the target category become sparser and contain more systematic gaps, learners show more conservatism in generalizing the allowable distributional properties to the novel word. Taken together, these results show striking flexibility in learners’ tendency to generalize, depending on the distributional properties of the input corpus, in a probabilistically rational way. Introduction The problem that learners face when they attempt to categorize items in the environment is deciding when they should treat instances as a category (thus generalizing from properties of experienced items to novel ones) and when they should treat instances separately (with no generalization from properties of experienced items to predicted properties of novel items). This problem cannot always be solved on the basis of perceptual similarity, as membership in some categories is independent of the surface features of the members. The acquisition of grammatical categories is an example of this type of problem, but has some additional complicating factors. We hear individual words in a limited number of specific contexts. However, the rules that languages are built on involve patterns defined over categories of words, not the individual words themselves. Language input is serially presented, so we need to predict the proper contexts for words we have not yet heard. Furthermore, learners never see the entire input corpus, so they must figure out the proper contexts for new words, keeping in mind that sometimes there are lexically specific restrictions on words (such as give versus donate: despite similar meaning, Joe can give David a book, but Joe cannot *donate David a book). In acquiring grammatical categories, the learner must ask whether contexts are absent by accident, or because they are ungrammatical. This question is particularly difficult to resolve when a new item is encountered in a single context and therefore overlaps only minimally with previously encountered words. For example, consider hearing the sentence: I remembered to nerk yesterday. Should one generalize from this context to another context where words of the category ‘verb’ are grammatical, such as She will make him nerk tomorrow, or I saw the cat nerk earlier? One hypothesis about how learners handle this situation is that they have innately defined linguistic categories with featural and contextual information predefined, so that minimal exposure to language is needed to sort out which words belong to each category (e.g., McNeill, 1966). Another hypothesis is that learners use semantic categories to bootstrap the syntactic categories (e.g., Grimshaw, 1981). A third possibility is that learners exploit distributional information in the input to discover the category structure of natural languages (e.g., Braine, 1987). This third hypothesis is what we investigate in the present experiments. A number of researchers have asked whether there is adequate distributional information in the input to form linguistic categories. This work uses hierarchical clustering and a computational learning mechanism to attempt to deduce grammatical categories from corpora of child- directed speech based solely on distributional analyses of the input (e.g., Mintz, Newport, & Bever, 2002; Redington, Chater, & Finch, 1998). These models have been able to use co-occurrence statistics among words to achieve relatively good categorization performance for frequent target words. To explore whether human learners can actually use this information during language learning, Mintz (2002) tested categorization in an artificial language learning environment, showing evidence that learners did engage in distributional analyses of the input in order to generalize their knowledge of previously encountered strings to grammatical novel strings. Hunt and Aslin (2010) showed that adults could learn categories embedded in sequences of visual symbols during a serial reaction time task when the only cue to category structure was distributional information among the symbol strings. Building off of these findings on the importance of distributional information for category formation, we have proposed a systematic set of computational variables that can explain the types of distributional information that are