Linguistic Semantics for Search Precision and Recall Improvement
published: April 15, 2010, recorded: September 2009, views: 3618
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Modern search engines return non-relevant documents too often. The main reason - search engines use algorithms based on various statistical scores of text documents, rather than on “understanding” the meaning of the queries and the contents of the documents. To understand the meaning of a query and documents we should use some semantic model which allows us to semantically match a query with documents. Since we deal with natural language documents we should use an adequate linguistic model and text representation model. The first lecture gives a general introduction to Linguistic Semantics, its brief history and definitions of natural language processing levels (lemmatizing, morphological, syntactic and semantic analysis). The introduction to Communicative Grammar is given. The second lecture gives an introduction to Heterogeneous Semantic Networks. We take into consideration text representation model in semantic search tasks: how Communicative Grammar and Heterogeneous Semantic Networks can be used for search precision and recall improvement.
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