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Found 6 papers, 1 papers with code
Learning to Rank from Relevance Judgments Distributions
Learning to Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair.
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations
Global annotations refer to global findings in the high-resolution image and do not include information about the location of the regions of interest or the magnification levels used to identify a finding.
Neural Feature Selection for Learning to Rank
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items.
A Relation Extraction Approach for Clinical Decision Support
In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature.
A Progressive Visual Analytics Tool for Incremental Experimental Evaluation
This paper presents a visual tool, AVIATOR, that integrates the progressive visual analytics paradigm in the IR evaluation process.
A Vector Space Model for Syntactic Distances Between Dialects
Syntactic comparison across languages is essential in the research field of linguistics, e. g. when investigating the relationship among closely related languages.
