Twitter からの人間行動属性の自動抽出  [in Japanese] Automatic Extraction of Human Activity Attributes from Twitter  [in Japanese]

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Author(s)

Abstract

本論文の目的は,twitterから取得した文中に現れる行動の基本属性(行動主,動作,対象,時間,場所)を自動的に抽出することである.しかし,先行研究では,頻度が低い行動を獲得できない.そして,抽出する前に,動詞リストとカテゴリワード(対象を表すワード)を予め準備しておく必要がある.そこで本論文では,条件付確率場(Conditional Random Fields)と自己教師あり学習(Self-Supervised Learning)を用いて,行動属性の自動抽出手法を提案する.提案手法では,人手でラベル編集や行動のドメインの定義などの必要がなく,頻度が低い行動も獲得できる.

The goal of this paper is to describe a method to automatically extract all basic attributes namely actor, action, object, time and location which belong to an activity, in each sentence retrieved from Twitter. Previous work had some limitations, such as inability of extracting infrequent activities, high setup cost, inability of extracting all attributes. To resolve these problems, this paper proposes a novel approach that treats the activity extraction as a sequence labeling problem, and automatically makes its own training data. This approach can extract infrequent activities, and has advantages such as domain-independence, scalability, and unnecessary hand-tagged data.

Journal

  • IEICE technical report

    IEICE technical report 110(105), 19-23, 2010-06-18

    The Institute of Electronics, Information and Communication Engineers

References:  26

Codes

  • NII Article ID (NAID)
    110007890098
  • NII NACSIS-CAT ID (NCID)
    AN10013061
  • Text Lang
    JPN
  • Article Type
    ART
  • ISSN
    09135685
  • NDL Article ID
    10751387
  • NDL Source Classification
    ZN33(科学技術--電気工学・電気機械工業--電子工学・電気通信)
  • NDL Call No.
    Z16-940
  • Data Source
    CJP  NDL  NII-ELS 
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