Many theorists have dismissed a priori the idea that distributional information could play a significant role in syntactic category acquisition. We demonstrate empirically that such information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms.We present a range of results using a large corpus of child-directed speech and explore their psychological implications. While our results show that a considerable amount of information concerning the syntactic categories can be obtained from distributional information alone, we stress that many other sources of information may also be potential contributors to the identification of syntactic classes.

The interrelation of childhood aggression, early and late adolescent delinquency, and drug use was explored. Data were obtained for the subjects when they were 5-10 years old. Follow-up interviews were conducted when the subjects were between 13-18 years old and again when they were 15-20 years old. A LISREL analysis of the three waves of data indicated that childhood aggression is a precursor of adolescent drug use and delinquency, and that early adolescent drug use is correlated with contemporaneous delinquency as well as with later drug use and delinquency.

In this paper we report on a set of computational tools with (n)SGML pipeline data flow for uncovering internal structure in natural language texts. The main idea behind the workbench is the independence of the text representation and text analysis phases. At the representation phase the text is converted from a sequence of characters to features of interest by means of the annotation tools. At the analysis phase those features are used by statistics gathering and inference tools for finding significant correlations in the texts. The analysis tools are independent of particular assumptions about the nature of the feature-set and work on the abstract level of featureelements represented as SGML items.

In this paper we describe an architecture and functionality of main components of a workbench for an acquisition of domain knowledge from large text corpora. The workbench supports an incremental process of corpus analysis starting from a rough automatic extraction and organization of lexico-semantic regularities and ending with a computer supported analysis of extracted data and a semiautomatic re nement of obtained hypotheses. For doing this the workbench employs methods from computational linguistics, information retrieval and knowledge engineering. Although the workbench is currently under implementation some of its components are already implemented and their performance is illustrated with samples from engineering for a medical domain.