Data Structure ?
316.59M
Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
トピック分類用の1万件のドイツ語ニュース記事。
引用:
@InProceedings{Schabus2017,
著者 = {ディートマール・シャブスとマルシン・スコフロンとマルティン・トラップ},
タイトル = {100万件の投稿: ドイツ語のオンラインディスカッションのデータセット},
書籍名 = {第40回情報検索に関する国際ACM SIGIR会議の議事録 (SIGIR)},
ページ = {1241 - 1244},
年 = {2017},
住所 = {日本、東京},
ドイ = {10.1145/3077136.3080711},
月 = 8月
}
@InProceedings{Schabus2018,
著者 = {ディートマール・シャブスとマルシン・スコフロン},
タイトル = {新聞ウェブサイトのモデレーションシステムの開発と導入に関する学術的 - 産業的展望},
書籍名 = {第11回国際言語資源と評価会議の議事録 (LREC)},
年 = {2018},
住所 = {日本、宮崎},
月 = 5月,
ページ = {1602 - 1605},
概要 = {この論文は、大手新聞ウェブサイトのユーザーコメントのモデレーションを支援する自然言語処理 (NLP) および情報検索システムの開発、導入、および使いやすさテストに関するアプローチと我々の経験を説明しています。我々は、産業指向の研究環境と学術的な研究環境のいくつかの違い、およびそれらがデータ収集とアノテーションプロセス、文書表現の選択、および機械学習手法の選択における決定に与える影響を強調しています。解決すべき問題と扱うデータが商業企業から来る分類結果について報告します。NLP研究に典型的なこの文脈で、関連する産業面について議論します。我々は、直面した課題とそれらを解決するために提案された解決策が、同様の環境で働いている他の人に洞察を与えることができると信じています。},
URL = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html}
}
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