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On the Combined Use of Extrinsic Semantic Resources for Medical Information Search
In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i. e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically enhanced inverted index documents, iii) contribute to a heuristical weighting technique in the query document matching process.
Vision-based techniques for gait recognition
We then provide motivations for a novel paradigm in biometrics-based human recognition, i. e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.
Image understanding and the web
As far as the processing of the contextual information is concerned, problems linked to the syntactic and semantic characterizations of the textual components are important to address in order to tackle the semantic gap.
A User Study to Investigate Semantically Relevant Contextual Information of WWW Images
In order to improve its quality, we highlight contextual information which is relevant for the semantic characterization of Web images and study its statistical properties in terms of its location and nature considering a classification into five semantic concept classes: signal, object, scene, abstract and relational.
A Linguistically Driven Framework for Query Expansion via Grammatical Constituent Highlighting and Role-Based Concept Weighting
In this paper, we propose a linguistically-motivated query expansion framework that recognizes and en-codes significant query constituents that characterize query intent in order to improve retrieval performance.
Coupled intrinsic and extrinsic human language resource-based query expansion
Poor information retrieval performance has often been attributed to the query-document vocabulary mismatch problem which is defined as the difficulty for human users to formulate precise natural language queries that are in line with the vocabulary of the documents deemed relevant to a specific search goal.
Coupling semantic and statistical techniques for dynamically enriching web ontologies
With the development of the Semantic Web technology, the use of ontologies to store and retrieve information covering several domains has increased.
Natural language technology and query expansion: issues, state-of-the-art and perspectives
Natural language queries consist of multiple linguistic features which serve to represent the intended search goal.
Leveraging Cognitive Search Patterns to Enhance Automated Natural Language Retrieval Performance
We formalize the application of these patterns by considering a query conceptual representation and introducing a set of operations allowing to operate modifications on the initial query.
