PubMed · 1559109
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Explore connections, maps & timelines
S Henshaw. 1992-03-07. Searching published reports.. https://doi.org/10.1136/bmj.304.6827.642-a
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We apply a version of our web-based literature-mapping system to PNAS for 1971-2002, as indexed by the National Library of Medicine and the Institute for Scientific Information. Given a single input term from a user, a medical subject heading, a cocited author, or a cocited journal, PNASLINK rapidly displays views in which that term and the other 24 terms that most frequently co-occur with it in a bibliographic database are interrelated in ways suggesting fruitful combinations for document retrieval. The interrelationships are produced by two algorithms, pathfinder networks and Kohonen-style self-organizing maps. PNASLINK displays are themselves interactive interfaces that can retrieve documents from digital libraries (e.g., PNAS Online). This style of visualizing knowledge domains is called "localized" because it does not attempt to map the indexing of literatures in full but concentrates on the top terms in an "associative thesaurus" reflecting user interests. It also permits swift remappings, as the user recognizes terms worth pursuing. PNASLINK is illustrated with maps drawn from the literature of population genetics. Some comparative and evaluative comments are added, one from a domain expert indicating that the face validity of the system may be tempered by insufficient specificity in the indexing terms being mapped.
Recent developments in question-answering systems have demonstrated that approaches based on propositional analysis of source text, in conjunction with formal inference systems, can produce substantive improvements in performance over surface-form approaches. [Voorhees, E. M. (2002) in Eleventh Text Retrieval Conference, eds. Voorhees, E. M. & Buckland, L. P., http://trec.nist.gov/pubs/trec11/t11_proceedings.html]. However, such systems are hampered by the need to create broad-coverage knowledge bases by hand, making them difficult to adapt to new domains and potentially fragile if critical information is omitted. To demonstrate how this problem might be addressed, the Syntagmatic Paradigmatic model, a memory-based account of sentence processing, is used to autonomously extract propositional knowledge from unannotated text. The Syntagmatic Paradigmatic model assumes that people store a large number of sentence instances. When trying to interpret a new sentence, similar sentences are retrieved from memory and aligned with the new sentence by using String Edit Theory. The set of alignments can be considered an extensional interpretation of the sentence. Extracting propositional information in this way not only permits the model to answer questions for which the relevant facts are explicitly stated in the text but also allows the model to take advantage of "inference by coincidence," where implicit inference occurs as an emergent property of the mechanism. To illustrate the potential of this approach, the model is tested for its ability to determine the winners of tennis matches as reported on the Association of Tennis Professionals web site.