this paper we present a natural language processing method for extracting causal relations between genetic phenomena and diseases. After presenting the results of a preliminary evaluation, we suggest the use of a graphical display application for viewing the semantic predications produced by the system

This paper first describes the methodology of journal descriptor (JD) indexing, based on human indexing at the journal level using only 127 descriptors, and applying statistical methods that associate this journal indexing with text words in a training set of MEDLINE citations. These associations form the basis for automatic indexing of documents outside the training set. The paper then presents the new technique of semantic type (ST) indexing, based on JD indexing associated with each of 134 ST's, and applying the standard cosine coefficient measure to compare the similarity between the JD indexing of a document and the JD indexing of each ST. The ST indexing of the document is the list of ST's ranked in decreasing order of similarity between the JD indexing of the document and the JD indexing of the ST's. Discussion of the potential usefulness and application of the very general indexing provided by JD's and ST's comprises the remainder of the paper. JD's have been used for more than thirty years to search MEDLINE by discipline, and discipline-based indexing is in evidence on the Web. It is suggested, with several examples, that ST's may convey a unique slant of a document's content not normally represented in standard indexing vocabularies. Use of ST indexing to rank retrieved output is mentioned as a possible application. Notwithstanding the importance of methodology and performance issues, the intent of this paper is to explore questions of the potential utility and applicability of JD and ST indexing

An experiment was performed at the National Library of Medicine ® (NLM ® ) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM’s MetaMap system, which maps free text to terms corresponding to concepts in NLM’s Unified Medical Language System ® (UMLS ® ) Metathesaurus ®. If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based on statistical associations between words in a training set of MEDLINE ® citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the

Abstract not found

The shift from paper to electronic documents has caused the curation of information sources in large electronic databases to become more generalized. In the biomedical domain, continuing efforts aim at refining indexing tools to assist with the update and maintenance of databases such as MEDLINE ®. In this paper, we evaluate two statistical methods of producing MeSH ® indexing recommendations for the genetics literature, including recommendations involving subheadings, which is a novel application for the methods. We show that a generic representation of the documents yields both better precision and recall. We also find that a domainspecific representation of the documents can contribute to enhancing recall. 1

this paper describes journal descriptor (JD) indexing, based on indexing at the journal level using only 127 descriptors, and applying statistical methods that associate this journal indexing with text words in a training set of MEDLINE citations. These associations then form the basis for automatic indexing of documents outside the training set. The paper then presents the new technique of semantic type (ST) indexing, based on JD indexing associated with each of 134 ST's, and applying the standard cosine coefficient measure to compare the similarity between the JD indexing of a document and the JD indexing of each ST. The ST indexing of the document is the list of ST's ranked in decreasing order of similarity between the JD indexing of the document and the JD indexing of the ST's. Discussion of the potential usefulness and application of the very general indexing provided by ST's comprises the remainder of the paper. In particular, it is suggested, with several examples, that ST's may convey a unique slant of a document's content not normally represented in standard indexing vocabularies. Use of ST indexing to rank retrieved output is mentioned as a possible application. Notwithstanding the importance of methodology and performance issues, the intent of this paper is to explore questions of the potential utility and applicability of ST indexing

Researchers in the biomedical and molecular biology fields are faced with a wide variety of information sources. These are presented in the form of images, free text, and structured data files that include medical records, gene and protein sequence data, and whole genome microarray data, all gathered from a variety of experimental organisms and clinical subjects.

Researchers in the biomedical and molecular biology fields are faced with a wide variety of information sources. These are presented in the form of images, free text, and structured data files that include medical records, gene and protein sequence data, and whole genome microarray data, all gathered from a variety of experimental organisms and clinical subjects. The need to organize and relate this information, particularly concerning genes, has motivated the development of resources, such as the Unified Medical Language System, Gene

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