Vector Space Representations in Information Retrieval

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Vector Space Representations in Information Retrieval

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Vector Space Representations in Information Retrieval

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/TD03000295" target="_blank" >TD03000295: Inteligentní software pro sémantické hledání dokumentů</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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