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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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