Vector Space Representations in Information Retrieval

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00094402" target="_blank" >RIV/00216224:14330/17:00094402 - isvavai.cz</a>

Result on the web

<a href="https://is.muni.cz/th/409729/fi_m/main.pdf" target="_blank" >https://is.muni.cz/th/409729/fi_m/main.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Vector Space Representations in Information Retrieval

Original language description

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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TD03000295" target="_blank" >TD03000295: Intelligent software for semantic text search</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů