Search (8 results, page 1 of 1)

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Abstract

Human indexing for information retrieval is intellectually labor intensive. It requires maintaining a system of indexing rules and policies, which in turn require maintaining a controlled indexing vocabulary. These activities are being performed at the National Library of Medicine in support of indexing the MEDLINE database using the MeSH thesaurus. An additional requirement of the conventional indexing operation is maintaining and developing a user interface, known as the Automated Indexing and Management System (AIMS). Describes knowledge-based indexing, based on a unique prototype, called MedIndEx (Medical Indexing Expert)

Source

Challenges in indexing electronic text and images. Ed.: R. Fidel et al

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Abstract

Interactice knowledge-based indexing of the National Library of Medicine's MEDLINE database is advocated. It is established that in the current setting concept indexing is needed and cannot be fully automated. Compatibility between conventional and knowledge-based indexing is highlighted, followed by discussion of indexing as a cognitive process. The section of knowledge-based indexing describes how NLM's MedIndEx prototype addresses problems in conventional indexing and includes the contention that constructing a knowledge base adapted from a conventional classified thesaurus and indexing scheme is not as daunting as it may seem. Extension of the prototype to an intelligent search assistant illustrates use of the same knowledge base to integrate indexing and retrieval applications. Suggested are also future directions for knowledge-based indeing

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Abstract

A new, fully automated approach for indedexing documents is presented based on associating textwords in a training set of bibliographic citations with the indexing of journals. This journal-level indexing is in the form of a consistent, timely set of journal descriptors (JDs) indexing the individual journals themselves. This indexing is maintained in journal records in a serials authority database. The advantage of this novel approach is that the training set does not depend on previous manual indexing of thousands of documents (i.e., any such indexing already in the training set is not used), but rather the relatively small intellectual effort of indexing at the journal level, usually a matter of a few thousand unique journals for which retrospective indexing to maintain consistency and currency may be feasible. If successful, JD indexing would provide topical categorization of documents outside the training set, i.e., journal articles, monographs, Web documents, reports from the grey literature, etc., and therefore be applied in searching. Because JDs are quite general, corresponding to subject domains, their most problable use would be for improving or refining search results

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Abstract

The MedIndEx (Medical Indexing Expert) research project combines artificial intelligence and information retrieval principles and methods to develop and test an interactive knowledge-based prototype for computer-assisted indexing of the MEDLINE database. By encoding the indexing scheme in a knowledge base, and designing a system for indexers to use in a workstation environment, the objective of this project is to facilitate "expert indexing" that is performed at the National Library of Medicine

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Source

Indexing, providing access to information: looking back, looking ahead. Proceedings of the 25th Annual Meeting of the American Society of Indexers. Ed.: N.C. Mulvany

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Abstract

Automatic document categorization is an important research problem in Information Science and Natural Language Processing. Many applications, including, Word Sense Disambiguation and Information Retrieval in large collections, can benefit from such categorization. This paper focuses on automatic categorization of documents from the biomedical literature into broad discipline-based categories. Two different systems are described and contrasted: CISMeF, which uses rules based on human indexing of the documents by the Medical Subject Headings (MeSH) controlled vocabulary in order to assign metaterms (MTs), and Journal Descriptor Indexing (JDI), based on human categorization of about 4,000 journals and statistical associations between journal descriptors (JDs) and textwords in the documents. We evaluate and compare the performance of these systems against a gold standard of humanly assigned categories for 100 MEDLINE documents, using six measures selected from trec_eval. The results show that for five of the measures performance is comparable, and for one measure JDI is superior. We conclude that these results favor JDI, given the significantly greater intellectual overhead involved in human indexing and maintaining a rule base for mapping MeSH terms to MTs. We also note a JDI method that associates JDs with MeSH indexing rather than textwords, and it may be worthwhile to investigate whether this JDI method (statistical) and CISMeF (rule-based) might be combined and then evaluated showing they are complementary to one another.

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Abstract

An overview of levels and approaches in the evalution of knowledge-based systems is presented. There is a need for empirical studies using objective criteria in advance of completing the technical evaluation of such systems. A methodology for this type of evaluation developed for a particular knowledge-based indexing system is presented. It is suggested that the proposed study may serve as a model for the design of any evaluation in which the results of existing intellectual procedures are compared with results achieved when these procedures are aided by use of an appropriate expert system

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Abstract

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 an 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 basis for selecting the best meaning that is correlated to UMLS semantic types (STs) assigned to ambiguous concepts in the Metathesaurus. For example, the ambiguity transport has two meanings: "Biological Transport" assigned the ST Cell Function and "Patient transport" assigned the ST Health Care Activity. A JDI-based methodology can analyze text containing transport and determine which ST receives a higher score for that text, which then returns the associated meaning, presumed to apply to the ambiguity itself. We then present an experiment in which a baseline disambiguation method was compared to four versions of JDI in disambiguating 45 ambiguous strings from NLM's WSD Test Collection. Overall average precision for the highest-scoring JDI version was 0.7873 compared to 0.2492 for the baseline method, and average precision for individual ambiguities was greater than 0.90 for 23 of them (51%), greater than 0.85 for 24 (53%), and greater than 0.65 for 35 (79%). On the basis of these results, we hope to improve performance of JDI and test its use in applications.