Refereed full papers (journals, book chapters, international conferences)

1997

@article{LCS1997,
	vgclass =	{refpap},
	author =	{Dik L. Lee and Huei Chuang and Kent Seamons},
	title =	{Document ranking and the vector-space model},
	journal =	{IEEE Software},
	volume =	{14},
	number =	{2},
	pages =	{67--75},
	year =	{1997},
	url =	{http://dx.doi.org/10.1109/52.582976},
	abstract =	{Efficient and effective text retrieval techniques are
	critical in managing the increasing amount of textual information
	available in electronic form. Yet text retrieval is a daunting task
	because it is difficult to extract the semantics of natural language
	texts. Many problems must be resolved before natural language
	processing techniques can be effectively applied to a large collection
	of texts. Most existing text retrieval techniques rely on indexing
	keywords. Unfortunately, keywords or index terms alone cannot
	adequately capture the document contents, resulting in poor retrieval
	performance. Yet keyword indexing is widely used in commercial systems
	because it is still the most viable way by far to process large amounts
	of text. Using several simplifications of the vector-space model for
	text retrieval queries, the authors seek the optimal balance between
	processing efficiency and retrieval effectiveness as expressed in
	relevant document rankings.},
}