{
  "id": 71361,
  "title": "Helpful Material to get started",
  "url": "/competitions/quora-insincere-questions-classification/discussion/71361",
  "author_name": "",
  "post_date": "2018-11-13T02:24:08.646253700Z",
  "votes": 58,
  "comment_count": 14,
  "views": 0,
  "content": "<p>These links are to some of the famous research papers from Google and Stanford for Toxic comment classification.<br></p>\n\n<p><a href=\"https://arxiv.org/pdf/1702.08138.pdf\">Deceiving Google’s Perspective API Built for Detecting Toxic Comments</a>\n<a href=\"https://web.stanford.edu/class/cs224n/reports/6856482.pdf\">Tackling Toxic comments online</a>\n<a href=\"https://web.stanford.edu/class/cs224n/reports/2762092.pdf\">Comment Abuse Classification with Deep Learning</a>\n<a href=\"https://arxiv.org/pdf/1802.09957.pdf\">Convolutional Neural Networks for Toxic Comment Classification</a></p>\n\n<p><br>\nFew links for Beginners\n<a href=\"https://www.kdnuggets.com/2018/10/more-effective-transfer-learning-nlp.html\">Getting started with transfer learning with NLP</a>\n<a href=\"https://course.fast.ai/lessons/lesson5.html\">Fast.ai getting started with NLP (LSTMs and RNN - lesson 5 and 6)</a>\n<a href=\"https://towardsdatascience.com/overview-of-transfer-learning-in-nlp-part-ii-fd2e8c72eb98\">Contextual Word Vectors</a></p>\n\n<p>Add any useful resource in the comment section.</p>",
  "messages": [
    {
      "id": "420069",
      "postDate": "11/13/2018 02:24:08",
      "content": "<p>These links are to some of the famous research papers from Google and Stanford for Toxic comment classification.<br></p>\n\n<p><a href=\"https://arxiv.org/pdf/1702.08138.pdf\">Deceiving Google’s Perspective API Built for Detecting Toxic Comments</a>\n<a href=\"https://web.stanford.edu/class/cs224n/reports/6856482.pdf\">Tackling Toxic comments online</a>\n<a href=\"https://web.stanford.edu/class/cs224n/reports/2762092.pdf\">Comment Abuse Classification with Deep Learning</a>\n<a href=\"https://arxiv.org/pdf/1802.09957.pdf\">Convolutional Neural Networks for Toxic Comment Classification</a></p>\n\n<p><br>\nFew links for Beginners\n<a href=\"https://www.kdnuggets.com/2018/10/more-effective-transfer-learning-nlp.html\">Getting started with transfer learning with NLP</a>\n<a href=\"https://course.fast.ai/lessons/lesson5.html\">Fast.ai getting started with NLP (LSTMs and RNN - lesson 5 and 6)</a>\n<a href=\"https://towardsdatascience.com/overview-of-transfer-learning-in-nlp-part-ii-fd2e8c72eb98\">Contextual Word Vectors</a></p>\n\n<p>Add any useful resource in the comment section.</p>",
      "rawMarkdown": "These links are to some of the famous research papers from Google and Stanford for Toxic comment classification.<br>\n\n[Deceiving Google’s Perspective API Built for Detecting Toxic Comments][1]\n[Tackling Toxic comments online][2]\n[Comment Abuse Classification with Deep Learning][3]\n[Convolutional Neural Networks for Toxic Comment Classification][4]\n\n<br>\nFew links for Beginners\n[Getting started with transfer learning with NLP][5]\n[Fast.ai getting started with NLP (LSTMs and RNN - lesson 5 and 6)][6]\n[Contextual Word Vectors][7]\n\nAdd any useful resource in the comment section.\n\n\n  [1]: https://arxiv.org/pdf/1702.08138.pdf\n  [2]: https://web.stanford.edu/class/cs224n/reports/6856482.pdf\n  [3]: https://web.stanford.edu/class/cs224n/reports/2762092.pdf\n  [4]: https://arxiv.org/pdf/1802.09957.pdf\n  [5]: https://www.kdnuggets.com/2018/10/more-effective-transfer-learning-nlp.html\n  [6]: https://course.fast.ai/lessons/lesson5.html\n  [7]: https://towardsdatascience.com/overview-of-transfer-learning-in-nlp-part-ii-fd2e8c72eb98",
      "votes": null
    },
    {
      "id": "420070",
      "postDate": "11/13/2018 02:25:30",
      "content": "<p>Very helpful! Thanks</p>",
      "rawMarkdown": "Very helpful! Thanks",
      "votes": null
    },
    {
      "id": "420079",
      "postDate": "11/13/2018 03:02:14",
      "content": "<p>Thank you so much.</p>",
      "rawMarkdown": "Thank you so much.",
      "votes": null
    },
    {
      "id": "420088",
      "postDate": "11/13/2018 03:21:07",
      "content": "<p>nice job！thanks</p>",
      "rawMarkdown": "nice job！thanks",
      "votes": null
    },
    {
      "id": "420148",
      "postDate": "11/13/2018 06:42:33",
      "content": "<p>great work</p>",
      "rawMarkdown": "great work",
      "votes": null
    },
    {
      "id": "420878",
      "postDate": "11/14/2018 09:23:44",
      "content": "<p>Thank you so much.</p>",
      "rawMarkdown": "Thank you so much.",
      "votes": null
    },
    {
      "id": "420915",
      "postDate": "11/14/2018 10:12:49",
      "content": "<p>Nice! Thank you so much.</p>",
      "rawMarkdown": "Nice! Thank you so much.",
      "votes": null
    },
    {
      "id": "421254",
      "postDate": "11/14/2018 19:43:37",
      "content": "<p>very helpful. Thanks!</p>",
      "rawMarkdown": "very helpful. Thanks!",
      "votes": null
    },
    {
      "id": "421268",
      "postDate": "11/14/2018 20:26:26",
      "content": "<p>great stuff, thanks!</p>",
      "rawMarkdown": "great stuff, thanks!",
      "votes": null
    },
    {
      "id": "421269",
      "postDate": "11/14/2018 20:26:32",
      "content": "<p>Awesome!</p>",
      "rawMarkdown": "Awesome!",
      "votes": null
    },
    {
      "id": "421315",
      "postDate": "11/14/2018 22:27:28",
      "content": "<p>Check out the following thread from a previous competition for identifying toxic comments. There are links to a lot of great resources on NLP!</p>\n\n<p><a href=\"https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/46073\">https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/46073</a></p>\n\n<p>I'd particularly recommend these links to get started:</p>\n\n<p><a href=\"https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a\">https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a</a>\n<a href=\"https://medium.com/@joshdotai/a-curated-list-of-speech-and-natural-language-processing-resources-4d89f94c032a\">https://medium.com/@joshdotai/a-curated-list-of-speech-and-natural-language-processing-resources-4d89f94c032a</a>\n<a href=\"https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e\">https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e</a></p>",
      "rawMarkdown": "Check out the following thread from a previous competition for identifying toxic comments. There are links to a lot of great resources on NLP!\n\nhttps://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/46073\n\nI'd particularly recommend these links to get started:\n\nhttps://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a\nhttps://medium.com/@joshdotai/a-curated-list-of-speech-and-natural-language-processing-resources-4d89f94c032a\nhttps://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e",
      "votes": null
    },
    {
      "id": "421460",
      "postDate": "11/15/2018 03:24:47",
      "content": "<p>Thank you for sharing :)</p>",
      "rawMarkdown": "Thank you for sharing :)",
      "votes": null
    },
    {
      "id": "428184",
      "postDate": "11/26/2018 22:14:36",
      "content": "<p>Thanks</p>",
      "rawMarkdown": "Thanks",
      "votes": null
    },
    {
      "id": "428348",
      "postDate": "11/27/2018 05:58:10",
      "content": "<p>nice job. Thanks!</p>",
      "rawMarkdown": "nice job. Thanks!",
      "votes": null
    },
    {
      "id": "1630036",
      "postDate": "12/26/2021 22:58:13",
      "content": "<p>thanks for sharing🙏</p>",
      "rawMarkdown": "thanks for sharing🙏",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1630036,
      "author_name": "denizm9",
      "author_url": "",
      "post_date": "12/26/2021 22:58:13",
      "content": "<p>thanks for sharing🙏</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 420070,
      "author_name": "fengsi",
      "author_url": "",
      "post_date": "11/13/2018 02:25:30",
      "content": "<p>Very helpful! Thanks</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 420079,
      "author_name": "amuchand47",
      "author_url": "",
      "post_date": "11/13/2018 03:02:14",
      "content": "<p>Thank you so much.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 420088,
      "author_name": "daijie23",
      "author_url": "",
      "post_date": "11/13/2018 03:21:07",
      "content": "<p>nice job！thanks</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 420148,
      "author_name": "sontiravi",
      "author_url": "",
      "post_date": "11/13/2018 06:42:33",
      "content": "<p>great work</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 420878,
      "author_name": "",
      "author_url": "",
      "post_date": "11/14/2018 09:23:44",
      "content": "<p>Thank you so much.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 420915,
      "author_name": "shitposter123",
      "author_url": "",
      "post_date": "11/14/2018 10:12:49",
      "content": "<p>Nice! Thank you so much.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 421254,
      "author_name": "mk9440",
      "author_url": "",
      "post_date": "11/14/2018 19:43:37",
      "content": "<p>very helpful. Thanks!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 421268,
      "author_name": "csa5040",
      "author_url": "",
      "post_date": "11/14/2018 20:26:26",
      "content": "<p>great stuff, thanks!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 421269,
      "author_name": "kostas2",
      "author_url": "",
      "post_date": "11/14/2018 20:26:32",
      "content": "<p>Awesome!</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 421315,
      "author_name": "bboyajian17",
      "author_url": "",
      "post_date": "11/14/2018 22:27:28",
      "content": "<p>Check out the following thread from a previous competition for identifying toxic comments. There are links to a lot of great resources on NLP!</p>\n\n<p><a href=\"https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/46073\">https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/46073</a></p>\n\n<p>I'd particularly recommend these links to get started:</p>\n\n<p><a href=\"https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a\">https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a</a>\n<a href=\"https://medium.com/@joshdotai/a-curated-list-of-speech-and-natural-language-processing-resources-4d89f94c032a\">https://medium.com/@joshdotai/a-curated-list-of-speech-and-natural-language-processing-resources-4d89f94c032a</a>\n<a href=\"https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e\">https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e</a></p>",
      "votes": null,
      "replies": [
        {
          "id": 421460,
          "author_name": "savannahar",
          "author_url": "",
          "post_date": "11/15/2018 03:24:47",
          "content": "<p>Thank you for sharing :)</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 428184,
      "author_name": "modmari",
      "author_url": "",
      "post_date": "11/26/2018 22:14:36",
      "content": "<p>Thanks</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 428348,
      "author_name": "horatiojsy",
      "author_url": "",
      "post_date": "11/27/2018 05:58:10",
      "content": "<p>nice job. Thanks!</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "420069": "These links are to some of the famous research papers from Google and Stanford for Toxic comment classification.<br>\n\n[Deceiving Google’s Perspective API Built for Detecting Toxic Comments][1]\n[Tackling Toxic comments online][2]\n[Comment Abuse Classification with Deep Learning][3]\n[Convolutional Neural Networks for Toxic Comment Classification][4]\n\n<br>\nFew links for Beginners\n[Getting started with transfer learning with NLP][5]\n[Fast.ai getting started with NLP (LSTMs and RNN - lesson 5 and 6)][6]\n[Contextual Word Vectors][7]\n\nAdd any useful resource in the comment section.\n\n\n  [1]: https://arxiv.org/pdf/1702.08138.pdf\n  [2]: https://web.stanford.edu/class/cs224n/reports/6856482.pdf\n  [3]: https://web.stanford.edu/class/cs224n/reports/2762092.pdf\n  [4]: https://arxiv.org/pdf/1802.09957.pdf\n  [5]: https://www.kdnuggets.com/2018/10/more-effective-transfer-learning-nlp.html\n  [6]: https://course.fast.ai/lessons/lesson5.html\n  [7]: https://towardsdatascience.com/overview-of-transfer-learning-in-nlp-part-ii-fd2e8c72eb98",
    "420070": "Very helpful! Thanks",
    "420079": "Thank you so much.",
    "420088": "nice job！thanks",
    "420148": "great work",
    "420878": "Thank you so much.",
    "420915": "Nice! Thank you so much.",
    "421254": "very helpful. Thanks!",
    "421268": "great stuff, thanks!",
    "421269": "Awesome!",
    "421315": "Check out the following thread from a previous competition for identifying toxic comments. There are links to a lot of great resources on NLP!\n\nhttps://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/discussion/46073\n\nI'd particularly recommend these links to get started:\n\nhttps://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a\nhttps://medium.com/@joshdotai/a-curated-list-of-speech-and-natural-language-processing-resources-4d89f94c032a\nhttps://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e",
    "421460": "Thank you for sharing :)",
    "428184": "Thanks",
    "428348": "nice job. Thanks!",
    "1630036": "thanks for sharing🙏"
  },
  "source": "meta"
}