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EMNLP
2009
13 years 2 months ago
Improving Web Search Relevance with Semantic Features
Most existing information retrieval (IR) systems do not take much advantage of natural language processing (NLP) techniques due to the complexity and limited observed effectivenes...
Yumao Lu, Fuchun Peng, Gilad Mishne, Xing Wei, Ben...
IJWGS
2010
116views more  IJWGS 2010»
13 years 2 months ago
Constructing Feature Vectors for search: investigating intrinsic quality impact on search performance
Abstract: In this paper, we revisit our approach to construction of semanticlinguistic Feature Vectors (FVs) used to enhance Web search. These FVs are built based on domain semanti...
Stein L. Tomassen, Darijus Strasunskas
SEMCO
2008
IEEE
13 years 11 months ago
Exploiting Semantic Query Context to Improve Search Ranking
One challenge for relevance ranking in Web search is underspecified queries. For such queries, top-ranked documents may contain information irrelevant to the search goal of the us...
Ziming Zhuang, Silviu Cucerzan
ICDE
2006
IEEE
128views Database» more  ICDE 2006»
13 years 11 months ago
Searching and Ranking Documents based on Semantic Relationships
Just as the link structure of the web is a critical component in today's web search, complex relationships (i.e., the different ways the dots are connected) will be an import...
Boanerges Aleman-Meza
ASWC
2009
Springer
13 years 9 months ago
Semantic-Linguistic Feature Vectors for Search: Unsupervised Construction and Experimental Validation
Abstract. In this paper, we elaborate on an approach to construction of semantic-linguistic feature vectors (FV) that are used in search. These FVs are built based on domain semant...
Stein L. Tomassen, Darijus Strasunskas