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@misc{1399341, author = {Novotný, Vít}, address = {Brno}, keywords = {document segmentation; synonymy; question answering; vector space model; text retrieval; information retrieval}, language = {eng}, location = {Brno}, publisher = {Fakulta Informatiky Masarykovy Univerzity}, title = {Vector Space Representations in Information Retrieval}, url = {https://is.muni.cz/th/409729/fi_m/main.pdf}, year = {2017} }
TY - GEN ID - 1399341 AU - Novotný, Vít PY - 2017 TI - Vector Space Representations in Information Retrieval PB - Fakulta Informatiky Masarykovy Univerzity CY - Brno KW - document segmentation KW - synonymy KW - question answering KW - vector space model KW - text retrieval KW - information retrieval UR - https://is.muni.cz/th/409729/fi_m/main.pdf N2 - Modern text retrieval systems employ text segmentation during the indexing of documents. I show that, rather than returning the segments to the user, significant improvements are achieved on the semantic text similarity task by combining all segments from a single document into one result with an aggregate similarity score. Standard text retrieval methods underestimate the semantic similarity between documents that use synonymous terms. Latent semantic indexing tackles the problem by clustering frequently co-occuring terms at the cost of the periodical reindexing of dynamic document collections and the suboptimality of co-occurences as a measure of synonymy. I develop a term similarity model that suffers neither of these flaws. ER -
NOVOTNÝ, Vít. \textit{Vector Space Representations in Information Retrieval}. Brno: Fakulta Informatiky Masarykovy Univerzity, 2017, 56 s.
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