Mike Symonds

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This paper reviews current knowledge on the role of the long-chain polyunsaturated fatty acids (LC-PUFA), docosahexaenoic acid (DHA, C22:6n-3) and arachidonic acid (AA, 20:4n-6), in maternal and term infant nutrition as well as infant development. Consensus recommendations and practice guidelines for health-care providers supported by the World Association(More)
Many existing information retrieval models do not explicitly take into account information about word associations. Our approach makes use of first and second order relationships found in natural language, known as syntagmatic and paradigmatic associations, respectively. This is achieved by using a formal model of word meaning within the query expansion(More)
Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these(More)
In information retrieval, a user’s query is often not a complete representation of their real information need. The user’s information need is a cognitive construction, however the use of cognitive models to perform query expansion have had little study. In this paper, we present a cognitively motivated query expansion technique that uses semantic features(More)
This paper outlines a novel approach for modelling semantic relationships within medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent research suggests that corpus-driven approaches are effective at automatically(More)
This paper develops a framework for classifying term dependencies in query expansion with respect to the role terms play in structural linguistic associations. The framework is used to classify and compare the query expansion terms produced by the unigram and positional relevance models. As the unigram relevance model does not explicitly model term(More)
This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for(More)
Michael Symonds1,3, Peter Bruza1, Guido Zuccon2, Bevan Koopman1,2, Laurianne Sitbon1, Ian Turner1 1Queensland University of Technology, Brisbane, QLD 4001 2Australian e-Health Research Centre, CSIRO, Brisbane, QLD 4001 michael.symonds@qut.edu.au; p.bruza@qut.edu.au; guido.zuccon@csiro.au bevan.koopman@csiro.au; laurianne.sitbon@qut.edu.au;(More)
Many successful query expansion techniques ignore information about the term dependencies that exist within natural language. However, researchers have recently demonstrated that consistent and significant improvements in retrieval effectiveness can be achieved by explicitly modelling term dependencies within the query expansion process. This has created an(More)
This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or(More)