比較文集約に基づくエンティティ間の順序関係推定による補間エンティティの発見  [in Japanese] Finding Intermediate Entities by Detecting Order of Entities Based on Aggregation of Comparative Sentences  [in Japanese]

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Abstract

本研究では,比較文のマイニングによってエンティティ間の順序関係を明らかにし,ある観点で見た場合に2つのものの間にあてはまるエンティティ(補間エンティティ)およびその系列の発見をする手法に取り組む.本稿では特に,"この店よりはおいしくて,あの店よりはリーズナブルな店を見つけたい","2つの本の間にあてはまるような難しさを持つ本を見つけたい",といった,主観的評価における補間エンティティを発見する手法を提案する.提案手法では,検索エンジンを用いて比較文を発見,集約し,(評価対象,比較対象,評価,極性)の組を抽出し,それに基づきグラフを生成する.そして,得られたグラフより補間エンティティおよびその系列を発見する.本稿ではグラフから補間エンティティおよびその系列を発見する手法として3つの手法を提案し,評価を行った.We propose a method of detecting intermediate entities or sequences between two examples by detecting order of entities based on comparative sentences. We focus on finding intermediate entities based on subjective evaluations. For example, "I want to find a restaurant that is better than this restaurant but is cheaper than that one." and "I want to find a book that is intermediate level between two books." The main idea of our proposed method is collecting comparative sentences with entities. First, it collects comparative sentences with particular expressions and extracts comparative relations from them and generates a directed graph of their relations. Finally, it finds an optimal path from the graph. We proposed three methods to rank nodes and paths and evaluated their effectiveness by conducting the experiments.

We propose a method of detecting intermediate entities or sequences between two examples by detecting order of entities based on comparative sentences. We focus on finding intermediate entities based on subjective evaluations. For example, "I want to find a restaurant that is better than this restaurant but is cheaper than that one." and "I want to find a book that is intermediate level between two books." The main idea of our proposed method is collecting comparative sentences with entities. First, it collects comparative sentences with particular expressions and extracts comparative relations from them and generates a directed graph of their relations. Finally, it finds an optimal path from the graph. We proposed three methods to rank nodes and paths and evaluated their effectiveness by conducting the experiments.

Journal

  • 情報処理学会論文誌

    情報処理学会論文誌 52(12), 3527-3541, 2011-12-15

    情報処理学会

Cited by:  1

Codes

  • NII Article ID (NAID)
    110008719929
  • NII NACSIS-CAT ID (NCID)
    AN00116647
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    1882-7764
  • NDL Article ID
    023426781
  • NDL Call No.
    YH247-743
  • Data Source
    CJPref  NDL  NII-ELS  IPSJ 
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