Context-specific Adaptation of Subjective Content Descriptions

Link:
Autor/in:
Verlag/Körperschaft:
IEEE
Erscheinungsjahr:
2021
Medientyp:
Text
Schlagworte:
  • "Domain Adaptation; Sentiment Classification; Transfer of Learning"
  • "Algorithms; Computer Vision; Models"
  • "Domain Adaptation; Sentiment Classification; Transfer of Learning"
  • "Algorithms; Computer Vision; Models"
  • Subjective content description
  • Domain adaptation
  • Topic Models
Beschreibung:
  • An agent in pursuit of a task may work with an individual collection of documents, which is known as a corpus. We assume that each document in the corpus is associated with additional location-specific data making the nearby content explicit by providing descriptions, references, or explanations about the content. Manually creating corpus- and location-specific data for documents is a time-consuming task. Thus, we are interested in using already existing data associated to documents in one corpus to enrich documents in another corpus without such data using the existing descriptions. This paper describes the problem for adapting location-specific data of documents in one corpus to documents in another corpus and presents an approach solving the problem. A case study shows the effectiveness of the adaptation approach.
Lizenz:
  • info:eu-repo/semantics/closedAccess
Quellsystem:
Forschungsinformationssystem der UHH

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Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/b2df6c5c-d770-462a-87e6-aacac679ec7a