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Podsumowanie opinii oparte na cechach przy użyciu uczenia transferowego

Podsumowanie opinii oparte na cechach przy użyciu uczenia transferowego

Abirami Ragupathi / Ramesh Sekaran

59,21 €
IVA incluido
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Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
Materia
Informática: cuestiones generales
ISBN:
9786209057823
59,21 €
IVA incluido
Disponible

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Wykorzystuje się analizę opinii, żeby pomóc nowym użytkownikom w podejmowaniu decyzji w różnych dziedzinach, jak produkty, filmy, media informacyjne, udostępnianie w sieciach społecznościowych itp. W większości istniejących metod analizy opinii opartej na cechach wykorzystuje się tylko korpus z jednej dziedziny. Analiza opinii oparta na cechach w dwóch różnych korpusach jest skomplikowana. Cechy i słowa opinii są wyodrębniane za pomocą narzędzia do tagowania części mowy (PoS). Technika wzajemnej zależności domen (IDDR) wykorzystuje usuwanie zbędnych cech i przycinanie nieistotnych cech z dwóch różnych domen za pomocą wyniku IDDR i wartości progowej. Zazwyczaj eksploracja danych i uczenie maszynowe wykorzystują dane szkoleniowe i testowe z tej samej domeny i mają tę samą cechę. Jednak powyższa koncepcja nie ma zastosowania we wszystkich domenach ze względu na brak oznaczonych zbiorów danych. W tym przypadku proponowana metoda uczenia transferowego wykorzystuje algorytm Exaggerate Instance weighted K nearest neighbor (EIWKNN) do przeniesienia wiedzy z domeny kamer do domeny iPodów w celu klasyfikacji opinii. Generowane jest podsumowanie cech dwóch różnych domen w odniesieniu do ich opinii.

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