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Substantial works have been conducted in automatic web query classification.
One of them is web query classification using labeled and unlabeled training data [17].
Another approach for web query classification uses machine learning techniques in an automatic classifier to categorize queries by geographical locality [18].
To address the problem of query classification, Bian and Chang [22] propose a label propagation method to automatically generate query class labels for unlabeled queries from click-based search logs.
OrthologID fails to identify node speciation/duplication events for query classification result.
PlantOrDB offers highly interactive visualization, accurate query classification and powerful search functions useful for functional genomic research.
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The representation of a set of documents as vectors in a common vector space is known as the vector space model and is fundamental to a host of information retrieval operations ranging from scoring documents on a query, document classification and document clustering.
Query type classification for web document retrieval is another related work [19].
Fig. 2 Effect of the consensus size on the consensus query global classification performance (AUC) and early recovery capability ((hbox {PM}_{1%})).
When showing gene families and query sequence classification results, PlantOrDB provides integrated graphical web interfaces to show the phylogenetic tree and sequence alignment synergically and interactively.
She also queried the classification of monogamy, saying the numbers seemed very high.
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