A Novel Regression Approach: Analyzing Textual Data in Similarity Space
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F24%3A39921485" target="_blank" >RIV/00216275:25530/24:39921485 - isvavai.cz</a>
Výsledek na webu
<a href="https://ieeexplore.ieee.org/document/10516346" target="_blank" >https://ieeexplore.ieee.org/document/10516346</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FRUCT61870.2024.10516346" target="_blank" >10.23919/FRUCT61870.2024.10516346</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Novel Regression Approach: Analyzing Textual Data in Similarity Space
Popis výsledku v původním jazyce
The proliferation of textual data, notably in the form of database records, calls for innovative methods of analysis that go beyond traditional numerical techniques. While least squares regression has been a cornerstone in quantitative data analysis, its applicability to textual data remains largely unexplored. This study aims to bridge this gap by introducing a similarity-based least squares method tailored for textual data. Drawing on the principles of similarity measures in text, such as semantic and syntactic closeness, we propose an extension to the conventional least squares framework. Our approach incorporates word-based similarity metrics into the least squares objective function, enabling the analysis of textual data in a manner coherent with its qualitative nature. The developed methodology is rigorously evaluated using both synthetic and real-world database records, demonstrating its efficacy in uncovering intricate relationships within textual data. Our findings open new avenues for textual data analysis, blending the precision of classical statistical methods with the subtleties of text similarity.
Název v anglickém jazyce
A Novel Regression Approach: Analyzing Textual Data in Similarity Space
Popis výsledku anglicky
The proliferation of textual data, notably in the form of database records, calls for innovative methods of analysis that go beyond traditional numerical techniques. While least squares regression has been a cornerstone in quantitative data analysis, its applicability to textual data remains largely unexplored. This study aims to bridge this gap by introducing a similarity-based least squares method tailored for textual data. Drawing on the principles of similarity measures in text, such as semantic and syntactic closeness, we propose an extension to the conventional least squares framework. Our approach incorporates word-based similarity metrics into the least squares objective function, enabling the analysis of textual data in a manner coherent with its qualitative nature. The developed methodology is rigorously evaluated using both synthetic and real-world database records, demonstrating its efficacy in uncovering intricate relationships within textual data. Our findings open new avenues for textual data analysis, blending the precision of classical statistical methods with the subtleties of text similarity.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10200 - Computer and information sciences
Návaznosti výsledku
Projekt
—
Návaznosti
R - Projekt Ramcoveho programu EK
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of the 35th Conference of Open Innovations Association FRUCT
ISBN
979-8-3503-4947-4
ISSN
2305-7254
e-ISSN
2305-7254
Počet stran výsledku
8
Strana od-do
596-603
Název nakladatele
IEEE (Institute of Electrical and Electronics Engineers)
Místo vydání
New York
Místo konání akce
Tampere
Datum konání akce
24. 5. 2024
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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