The operation and performance of an artificially intelligent keywording system
Information Processing and Management: an International Journal
Retrieving terms and their variants in a lexicalized unification-based framework
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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We talk, in this paper, about the operation of indexation at INIST. We present two experiments carried out within the Department of Research and New Products that aim at the automation of indexation process. The first one comes within the scope of scientometric studies on text database. We have developped a software toolbox with which we can build a chain of treatments up to the generation of hyperdocuments. Therefore, indexation from a large corpus of source documents is the first module of that chain. In this part, we use linguistic and statistical methods to produce keywords from a stream of data. Linguitic heuristics are used to extract compound nouns or noun phrases from the text and combinational treatments determine the importance of each term according to the document. Keywords are here the input of an hypertext system. The second one is the development of a workstation for the information specialist integrating a computer-aided indexing system on title and abstract in bibliographical records. This indexing process works on a single bibliographical record and combines both linguistic methods and artificial intelligence (keywords generation). We use the same extraction module based on linguistic and add a knowledge based system to deduce implicit keywords. Finally, we show that the original specifications and purpose of each experiment are different and we start a discussion on the interest of these methods in relation to the kind of indexation wanted and the qualities expected from automatic indexing systems.