Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language. CHR-related research has surged during the decade following the previous survey by Frühwirth (1998). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis to systems, extensions and applications.

The need for processing biological information is rapidly growing, owing to the masses of new information in digital form being produced at this time. Old methodologies for processing it can no longer keep up with this rate of growth. The methods of Artificial Intelligence (AI) in general and of language processing in particular can offer much towards solving this problem. However, interdisciplinary research between language processing and molecular biology is not yet widespread, partly because of the effort needed for each specialist to understand the other one’s jargon. We argue that by looking at the problems of molecular biology from a language processing perspective, and using constraint based logic methodologies we can shorten the gap and make interdisciplinary collaborations more effective. We shall discuss several sequence analysis problems in terms of constraint based formalisms such Concept Formation Rules, Constraint Handling Rules (CHR) and their grammatical counterpart, CHRG. We postulate that genetic structure analysis can also benefit from these methods, for instance to reconstruct from a given RNA secondary structure, a nucleotide sequence that folds into it. Our proposed methodologies lend direct executability to high level descriptions of the problems at hand and thus contribute to rapid while efficient prototyping.

Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting erroneous reports. A solution to this problem is post-speech-recognition error detection that will assist the radiologist in proofreading more efficiently. In this paper, we present a statistical method for error detection that can be applied after transcription. The results are encouraging, showing an error detection rate as high as 96 % in some cases. KEY WORDS: Speech recognition, error detection, radiology reporting, co-occurrence relations, statistical natural language processing, computer-assisted proofreading

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Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language. CHR-related research has surged during the decade following the previous survey by Frühwirth (1998). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis