支配従属構造照合による文と名詞句の前方照応解析  [in Japanese] Anaphora Resolution of Sentences and Noun Phrases by Matching Dependency Structures  [in Japanese]

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Author(s)

    • JELINEK Jiri
    • シャープ(株)情報商品開発研究所 Information Systems Product Development Laboratories. SHARP Corp.

Abstract

本稿では, 文とその後方に位置する名詞句との照応を, 複雑な知識や処理機構を用いず, 表層的な情報を用いた簡単な処理によって解析する方法を提案する. 文と名詞句の構文構造を支配従属構造で表現し, それらの構造照合を行ない, 照合がとれた場合, 照応が成立するとみなす. 構造照合に用いる規則は, 文が名詞句に縮約されるときに観察される現象のうち, 主に, 用連助詞から体連助詞への変化, 情報伝達に必須でない語の削除に着目して定義する. このような簡単な処理によって前方照応がどの程度正しく捉えられるかを検証するための実験を, サ変動詞が主要部である文と, そのサ変動詞の語幹が主要部である名詞句の組を対象として行なった. 実験では, 新聞記事から抽出した178組のうち133組 (74.7%) について, 本手法による判定と人間による判定が一致した. また, 構造照合で類似性が最も高いと判断された支配従属構造の組を優先解釈として出力することによって, 入力の時点で一組当たり平均3.4通り存在した曖昧性が1.8通りへ絞り込まれた.

We propose a simple method of analysing correferentiality between sentences and later occurring noun phrases. Our method uses surface information and requires no complex data or processing mechanism. We represent a sentence and a later occurring noun phrase as dependency structures, and examine whether the two structures are matched. Where a matching between them can be established, we assume that the two are correferential. The rules for establishing structural matching are part of the paradigm of theme packing, namely the predictable changes of adverbal particles into adnominal particles and the disappearance of some non-essential information. In order to ascertain to what degree anaphora can be correctly traced by such simple processing, we have carried out an experiment centred upon sentences governed by a verb of the SAHEN category and later occurring noun phrases in which the head noun is formally identical with the invariable part of the SAHEN verb. Of the 178 pairs of such sentences and noun phrases selected from newspaper articles, 133 pairs (74.7%) were correctly identified as correferential or otherwise, in accordance with human judgement. Furthermore, as a side effect, the number of dependency structures to be considered can be reduced by selecting only the pairs of dependency structures with the best affinity through structural matching. By this method the average 3.4-fold structural ambiguity was reduced to average 1.8-fold.

Journal

  • Journal of Natural Language Processing

    Journal of Natural Language Processing 4(1), 111-123, 1997-01-10

    The Association for Natural Language Processing

References:  7

Cited by:  1

Codes

  • NII Article ID (NAID)
    10008827182
  • NII NACSIS-CAT ID (NCID)
    AN10472659
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    13407619
  • Data Source
    CJP  CJPref  J-STAGE 
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