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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)
Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source: Abstract. Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive(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)
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)
A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques ignore information about the dependencies that exist between words in natural language. However,(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)
(2011) Tensor query expansion : a cognitively motivated relevance model. Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source: Abstract In information retrieval, a user's query is often not a(More)
Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source: Abstract Many existing information retrieval models do not explicitly take into account information about word associations. Our approach makes(More)