Theory and practice in the construction of efficient interpreters

Various characteristics of a programming language, or of the hardware on which it is to be implemented, may make interpretation a more attractive implementation technique than compilation into machine instructions. Many interpretive techniques can be employed; this thesis is mainly concerned with an efficient and flexible technique using a form of interpretive code known as indirect threaded code (ITC). An extended example of its use is given by the Setl-s implementation of Setl, a programming language based on mathematical set theory. The ITC format, in which pointers to system routines are embedded in the code, is described and its extension to cope with polymorphic operators. The operand formats and some of the system routines are described in detail to illustrate the effect of the language design on the interpreter. Setl must be compiled into indirect threaded code and its elaborate syntax demands the use of a sophisticated parser. In Setl-s an LR(1) parser is implemented as a data structure which is interpreted in a way resembling that in which ITC is interpreted at runtime. Qualitative and quantitative aspects of the compiler, interpreter and system as a whole are discussed. The semantics of a language can be defined mathematically using denotational semantics. By setting up a suitable domain structure, it is possible to devise a semantic definition which embodies the essential features of ITC. This definition can be related, on the one hand to the standard semantics of the language, and on the other to its implementation as an ITC-based interpreter. This is done for a simple language known as X10. Finally, an indication is given of how this approach could be extended to describe Setl-s, and of the insight gained from such a description. Some possible applications of the theoretical analysis in the building of ITC-based interpreters are suggested

Prospectives

Tiré de: Prospectives, vol. 19, no 1/2/3, février/avril/oct. 1983.Titre de l'écran-titre (visionné le 24 janv. 2013

Prospectives

Tiré de: Prospectives, vol. 19, no 1/2/3, février/avril/oct. 1983.Titre de l'écran-titre (visionné le 24 janv. 2013

Special Libraries, September 1976

Volume 67, Issue 9https://scholarworks.sjsu.edu/sla_sl_1976/1007/thumbnail.jp

C.S.O. newsletter

In 1976 with v.4, no.1, began renumbering with January issue

Humanistiske data nr 2 1987

publishedVersio

Derivation of graph and pointer algorithms

We introduce operators and laws of an algebra of formal languages, a subalgebra of which corresponds to the algebra of (multiary) relations. This algebra is then used in the formal specification and derivation of some graph and pointer algorithms

Stylistic atructures: a computational approach to text classification

The problem of authorship attribution has received attention both in the academic world (e.g. did Shakespeare or Marlowe write Edward III?) and outside (e.g. is this confession really the words of the accused or was it made up by someone else?). Previous studies by statisticians and literary scholars have sought "verbal habits" that characterize particular authors consistently. By and large, this has meant looking for distinctive rates of usage of specific marker words -- as in the classic study by Mosteller and Wallace of the Federalist Papers. The present study is based on the premiss that authorship attribution is just one type of text classification and that advances in this area can be made by applying and adapting techniques from the field of machine learning. Five different trainable text-classification systems are described, which differ from current stylometric practice in a number of ways, in particular by using a wider variety of marker patterns than customary and by seeking such markers automatically, without being told what to look for. A comparison of the strengths and weaknesses of these systems, when tested on a representative range of text-classification problems, confirms the importance of paying more attention than usual to alternative methods of representing distinctive differences between types of text. The thesis concludes with suggestions on how to make further progress towards the goal of a fully automatic, trainable text-classification system

Stylistic atructures: a computational approach to text classification

The problem of authorship attribution has received attention both in the academic world (e.g. did Shakespeare or Marlowe write Edward III?) and outside (e.g. is this confession really the words of the accused or was it made up by someone else?). Previous studies by statisticians and literary scholars have sought "verbal habits" that characterize particular authors consistently. By and large, this has meant looking for distinctive rates of usage of specific marker words -- as in the classic study by Mosteller and Wallace of the Federalist Papers. The present study is based on the premiss that authorship attribution is just one type of text classification and that advances in this area can be made by applying and adapting techniques from the field of machine learning. Five different trainable text-classification systems are described, which differ from current stylometric practice in a number of ways, in particular by using a wider variety of marker patterns than customary and by seeking such markers automatically, without being told what to look for. A comparison of the strengths and weaknesses of these systems, when tested on a representative range of text-classification problems, confirms the importance of paying more attention than usual to alternative methods of representing distinctive differences between types of text. The thesis concludes with suggestions on how to make further progress towards the goal of a fully automatic, trainable text-classification system