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We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed.
Some examples of document similarity are document clustering, document categorization, document summarization, and query-based search.
IT-Sim (information-theoretic measure) for document similarity, proposed in [17, 18].
This paper presents a multi-level matching method for document retrieval (DR) using a hybrid document similarity.
We employ a filtering procedure based on document similarity, to obtain tweets that revolve around a specified topic (query).
A classical example is the tf-idf (term frequency - inverse document frequency) document similarity used in document search.
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A review of the major historical and current TRISO FPMs assesses each model's capabilities and origin and provides a systematic comparison of the codes to document similarities and differences in their features.
Whilst large data sets may be needed to provide a robust normative framework for clinical assessment and treatment prediction, we demonstrate the feasibility of collecting ERP data from large numbers of African children, and document similarities and subtle differences in the general developmental profile with those documented in Western populations.
We propose a novel knowledge-based technique for inter-document similarity computation, called Context Semantic Analysis (CSA).
In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively.
In addition, as has been well documented, similarity measures depend critically on the molecular representations and similarity coefficients used [31, 32].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com