Multi-Entity-Topic Models with Who-entities and Where-entities

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説明

<p>Conveying information about who, what, when and where is a primary purpose of news articles. To handle such information, statistical models that capture dependencies between named entities and topics can serve an important role. Although some relationships between who and where should be mentioned in a news story, no topic models explicitly addressed the textual interactions between a who-entity anda where-entity. This paper presents a new statistical model that directly captures dependencies between topics, who-entities and where-entities mentioned in each article. We show, through our experiments, how this multi-entity-topic model performs better at making predictions on who-entities.</p>

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詳細情報 詳細情報について

  • CRID
    1390570699999381120
  • NII論文ID
    130008079502
  • DOI
    10.11517/jsaisigtwo.2007.dmsm-a702_13
  • ISSN
    24365556
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
    • CiNii Articles
    • KAKEN
  • 抄録ライセンスフラグ
    使用可

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