Thesauruses are useful resources for NLP; however, manual construction of thesaurus is time consuming and suffers low coverage. Automatic thesaurus construction is developed to solve the problem. Conventional way to automatically construct thesaurus is by finding similar words based on context vector models and then organizing similar words into thesaurus structure. But the context vector metho...

The National Cancer Institute has developed the NCI Thesaurus, a biomedical vocabulary for cancer research, covering terminology across a wide range of cancer research domains. A major design goal of the NCI Thesaurus is to facilitate translational research. We describe: the features of Ontylog, a description logic used to build NCI Thesaurus; our methodology for enhancing the terminology throu...

Many terminologies are used in France for coding cancer diagnoses (ICD-10 for Diagnosis Related Groups, ICDO3 in cancer registries, ADICAP for pathological anatomy). This heterogeneity largely hinders the use of registries’ data. It is thus necessary to integrate the diagnostic terminologies in oncology into a unified and structured system. The NCI Thesaurus is an international terminology, whi...

Lexical resources are machine-readable dictionaries or lists of words, where semantic relationships between the terms are somehow expressed. These lexical resources have been used for many tasks such as word sense disambiguation and determining semantic similarity between terms. In recent years some research has been put into automatically building lexical resources from large corpora. In this ...

This paper describes the Laurin thesaurus, which is used for indexing and searching in the Laurin system, a software package for digital clipping archives. As a multilingual thesaurus it complies with the corresponding standards, though presenting some approaches going beyond some of the standards’ recommendations. The Laurin thesaurus integrates all kind of indexing terms, not only keywords, b...

We propose a method which, given a document to be classified, automatically generates an ordered set of appropriate descriptors extracted from a thesaurus. The method creates a Bayesian network to model the thesaurus and uses probabilistic inference to select the set of descriptors having high posterior probability of being relevant given the available evidence (the document to be classified). ...