Biomedical subjects

N Grabar

Publications and source records attributed to N Grabar.

5 recordsLinked to original sources

PubMed2002

An assessment of the visibility of MeSH-indexed medical web catalogs through search engines.

Manually indexed Internet health catalogs such as CliniWeb or CISMeF provide resources for retrieving high-quality health information. Users of these quality-controlled subject gateways are most often referred to them by general search engines such as Google, AltaVista, etc. This raises several questions, among which the following: what is the relative visibility of medical Internet catalogs through search engines? This study addresses this issue by measuring and comparing the visibility of six major, MeSH-indexed health catalogs through four different search engines (AltaVista, Google, Lycos, Northern Light) in two languages (English and French). Over half a million queries were sent to the search engines; for most of these search engines, according to our measures at the time the queries were sent, the most visible catalog for English MeSH terms was CliniWeb and the most visible one for French MeSH terms was CISMeF.

Abstracting and Indexing↗

PubMed2001

Building a text corpus for representing the variety of medical language.

Medical language processing has focused until recently on a few types of textual documents. However, a much larger variety of document types are used in different settings. It has been showed that Natural Language Processing (NLP) tools can exhibit very different behavior on different types of texts. Without better informed knowledge about the differential performance of NLP tools on a variety of medical text types, it will be difficult to control the extension of their application to different medical documents. We endeavored to provide a basis for such informed assessment: the construction of a large corpus of medical text samples. We propose a framework for designing such a corpus: a set of descriptive dimensions and a standardized encoding of both meta-information (implementing these dimensions) and content. We present a proof of concept demonstration by encoding an initial corpus of text samples according to these principles.

Documentation↗

PubMed2001

The contribution of morphological knowledge to French MeSH mapping for information retrieval.

MeSH-indexed Internet health directories must provide a mapping from natural language queries to MeSH terms so that both health professionals and the general public can query their contents. We describe here the design of lexical knowledge bases for mapping French expressions to MeSH terms, and the initial evaluation of their contribution to Doc'CISMeF, the search tool of a MeSH-indexed directory of French-language medical Internet resources. The observed trend is in favor of the use of morphological knowledge as a moderate (approximately 5%) but effective factor for improving query to term mapping capabilities.

Algorithms↗

PubMed2000

A general method for sifting linguistic knowledge from structured terminologies.

Morphological knowledge is useful for medical language processing, information retrieval and terminology or ontology development. We show how a large volume of morphological associations between words can be learnt from existing medical terminologies by taking advantage of the semantic relations already encoded between terms in these terminologies: synonymy, hierarchy and transversal relations. The method proposed relies on no a priori linguistic knowledge. Since it can work with different relations between terms, it can be applied to any structured terminology. Tested on SNOMED and ICD in French and English, it proves to identify fairly reliable morphological relations (precision > 90%) with a good coverage (over 88% compared to the UMLS lexical variant generation program). For English words with a stem longer than 3 characters, recall reaches 98.8% for inflection and 94.7% for derivation.

Linguistics↗

PubMed1999

Language-independent automatic acquisition of morphological knowledge from synonym pairs.

Medical words exhibit a rich and productive morphology. Beyond simple inflection, derivation and composition are a common way to form new words. Morphological knowledge is therefore very important for any medical language processing application. Whereas rich morphological resources are available for the English medical language with the UMLS Specialist Lexicon, no such resources are publicly available for French or most other languages. We propose a simple and powerful method to help acquire automatically such knowledge. This method takes advantage of the synonym terms present in medical terminologies. In a bootstrapping step, it detects morphologically related words from which it learns "derivation rules". In an expansion step, it then applies these rules to the whole vocabulary available. Our goal is to acquire data for French and other languages for which they are not available. However, to evaluate the efficiency of the method, we tested it on English in a setting which is close to that prevailing for French, and we confronted its results to those obtained with the Specialist lexical variant generation tool.

Electronic Data Processing↗