{
  "fetched_at": "2026-10-20T08:22:28Z",
  "source_url": "https://www.kaggle.com/competitions/english-sa-competition-bdc101/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2>Data Description</h2>\n<p>\"Friends\"는 드라마 대본의 각 발화에 감정을 레이블링한 오픈 데이터셋입니다.</p>\n<p>train, dev, test 셋으로 구성되어 있고, 각각 720, 80, 200개의 대화로 만들어져 있습니다.</p>\n<p>레이블은 총 8개의 class 중 하나로 분류되어 있습니다: neutral, joy, sadness, fear, anger, surprise, disgust, non-neutral</p>\n<h2>File Description</h2>\n<h3>Train</h3>\n<p>학습 데이터 다운로드: <a href=\"http://doraemon.iis.sinica.edu.tw/emotionlines/download.html\">download</a></p>\n<h3>Test</h3>\n<p>data.csv - 입력으로 사용할 테스트 파일</p>\n<p>sample.csv - 예측값을 레이블링해 제출할 테스트 파일 (B1 셀의 \"Expected\"를 \"Predicted\"로 변경해 제출바랍니다.)</p>\n<table>\n      <caption>제출 파일 예시</caption>\n      <thead>\n        <tr>\n          <th>Id</th>\n          <th>Predicted</th>\n        </tr>\n      </thead>\n      <tbody>\n        <tr>\n          <td>0</td>\n          <td>neutral</td>\n        </tr>\n        <tr>\n          <td>1</td>\n          <td>joy</td>\n        </tr>\n        <tr>\n          <td>2</td>\n          <td>sadness</td>\n        </tr>\n      </tbody>\n</table>",
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      "name": "Description",
      "content": "<h1>\nNatural Language Processing Task 2\n</h1>\n\n<p>\n주어진 대화로부터 각 발화의 감정을 분류하는 task입니다.\n</p>\n\n<p>\n오픈 데이터셋인 \"Friends\" 데이터셋을 학습 데이터로 이용합니다. (데이터 다운로드: <a href=\"http://doraemon.iis.sinica.edu.tw/emotionlines/download.html\">download</a>)\n</p>\n\n<p>\nPublic 리더보드의 순위는 성적에 반영되지 않으며, Private data로 최종 성능을 평가합니다.\n</p>\n\n<p>\nDue date: <b>2020-12-23 23:59:00 (UTC)</b><br>\nMaximum Daily Submissions: 20\n</p>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top (the page title appears above). For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have copied here:</p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.</p>\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids.&nbsp;For example, if you suspect author A, B, and C are the same, you should list (A,A B C), (B,B A C), (C,C A B).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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