We define a new formal grammatical system called a link grammar . A sequence of words is in the language of a link grammar if there is a way to draw links between words in such a way that (1) the local requirements of each word are satisfied, (2) the links do not cross, and (3) the words form a connected graph. We have encoded English grammar into such a system, and written a program (based on new algorithms) for efficiently parsing with a link grammar. The formalism is lexical and makes no explicit use of constituents and categories. The breadth of English phenomena that our system handles is quite large. A number of sophisticated and new techniques were used to allow efficient parsing of this very complex grammar. Our program is written in C, and the entire system may be obtained via anonymous ftp. Several other researchers have begun to use link grammars in their own research. 1 Introduction Most sentences of most natural languages have the property that if arcs are drawn connecti...

This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community. and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one on learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toe-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.

We develop a formal grammatical system called a link grammar, show how English grammar can be encoded in such a system, and give algorithms for efficiently parsing with a link grammar. Although the expressive power of link grammars is equivalent to that of context free grammars, encoding natural language grammars appears to be much easier with the new system. We have written a program for general link parsing and written a link grammar for the English language. The performance of this preliminary system – both in the breadth of English phenomena that it captures and in the computational resources used – indicates that the approach may have practical uses as well as linguistic significance. Our program is written in C and may be obtained through the internet.

Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects. Keywords: