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- W1973715534 abstract "Semantic associations take many forms, sometimes being explicit as in visible links and at other times being implicit, not visible but nevertheless clear to the human reader. Some implicit semantic associations might be calculable as the result of a computation but in some cases it is difficult for a computation to capture the purpose of a semantic association, for example, the semantic similarities embodied by synonyms and similar word/phrase likenesses are not easily specified in a general rule. It is possible however to capture semantic associations made by human searchers. Searchers interact with search results by clicking on one or more resources in a set of results, and this interaction takes two forms: the first being an implicit indication of the relevance of the search term to the chosen resource, and the second being an implicit indication of the mutual relevance of any two or more resources selected from the same search. Both have been proposed as a similarity measure for clustering of resources. In this paper we implement, evaluate and compare three methods for semantic association discovery, mined from Web search logs. The first method is based purely on query analysis, the second is single click-based, and the third is coselection-based. The methods are compared for their effectiveness at detecting semantic similarities." @default.
- W1973715534 created "2016-06-24" @default.
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- W1973715534 date "2013-05-01" @default.
- W1973715534 modified "2023-09-25" @default.
- W1973715534 title "Discovering semantic associations from web search interactions" @default.
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- W1973715534 doi "https://doi.org/10.1145/2481492.2481517" @default.
- W1973715534 hasPublicationYear "2013" @default.
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