Data Structure ?
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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
Task 4の主なタスクは、簡単な名詞（名詞または基本的な名詞句）間の意味関係の分類です。例えば、「蜜蜂」は、製品生産者関係の一例を示しています。この分類は、書かれた英語の文章の文脈の中で行われます。意味関係分類アルゴリズムは、情報検索、情報抽出、テキスト要約、質問応答などの分野に適用できます。テキスト含意（TatuとMoldovan, 2005）の認識は、この種の深入りした分析を高度な自然言語処理（NLP）アプリケーションで成功裡に使用する例です。
ファイル
サイズ：小さなデータセットで、7つの関係タイプと合計1529個の注釈付きの例が含まれています。
例：
関連論文
1.T. ChklovskiとP. Pantel. 2004. Verbocean: ウェブから細粒度の意味的動詞関係を掘り起こす. 自然言語処理における経験的手法に関する会議録（Proc. Conf. on Empirical Methods in Natural Language Processing, EMNLP - 04）, 33 - 40頁, スペインのバルセロナ. 2.R. Girju, D. Moldovan, M. Tatu, およびD. Antohe. 2005. 名詞複合語の意味論について. コンピュータ音声と言語（Computer Speech and Language）, 19:479 - 496.
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- 1. The data originates from internet data collection or provided by service providers, and this platform offers users the ability to view and browse datasets.
- 2. This platform serves only as a basic information display for datasets, including but not limited to image, text, video, and audio file types.
- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
- 1. All datasets on this site are copyrighted by their original publishers or data providers.
- 1. If you need to repost data from this site, please retain the original data source URL and related copyright notices.
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