{
  "fetched_at": "2026-10-19T07:22:45Z",
  "source_url": "https://www.kaggle.com/competitions/billion-word-imputation/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>The data for this competition is a large corpus of English language sentences. You should use only the sentences in the training set to build you model.</p>\r\n<p>We have removed one word from each sentence in the test set. The location of the removed word was chosen uniformly randomly and is never the first or last word of the sentence (in this dataset, the last word is always a period). You must attempt to submit the sentences in the test set with the correct missing word located in the correct location. </p>\r\n<p>Note: the train/test split used in this competition is different than the published version used for language modeling. If you are creating full language models and scoring perplexity, you should download the official version of the corpus from the authors' website.</p>\r\n<h2>File descriptions</h2>\r\n<ul>\r\n<li><strong>train.txt</strong> - the training set, contains a large collection of English language sentences</li>\r\n<li><strong>test.txt</strong> - the test set, contains a large number of sentences where one word has been removed</li>\r\n</ul>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "<p><img style=\"float: left; margin: 0px 15px 15px 0px;\" src=\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/3927/media/corpus.png\" alt=\"\" width=\"148\" height=\"856\" />This competition uses the billion-word benchmark corpus provided by <a href=\"http://arxiv.org/abs/1312.3005\">Chelba et al.</a> for language modeling. Rather than ask participants to create a classic language model and evaluate sentence probabilities -- a task which is difficult to faithfully score in Kaggle's supervised ML setting -- we have introduced a variation on the language modeling task.</p>\r\n<p>For each sentence in the test set, we have removed exactly one word. Participants must create a model capable of inserting back the correct missing word at the correct location in the sentence. Submissions are scored using an edit distance to allow for partial credit.</p>\r\n<p>We extend our thanks to authors who created this corpus and shared it for the research community to use. Please cite this paper if you use this dataset in your research: <em>Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn: One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling, CoRR, 2013.</em></p>\r\n<p>Note: the train/test split used in this competition is different than the published version used for language modeling. If you are creating full language models and scoring perplexity, you should download the official version of the corpus from the authors' website.</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<p>Submissions are evaluated on the mean <a href=\"http://en.wikipedia.org/wiki/Levenshtein_distance\">Levenshtein distance</a> between the sentences you submit and the original sentences in the test set.</p>\r\n<p><em>Note: due to the size and computations necessary to score submissions for this competition, scoring may take 5-10 minutes, and possibly longer if there are other submissions in front of yours. Please be patient!</em></p>\r\n<h2>Submission File</h2>\r\n<p>Your submission file should contain the sentence id and a predicted sentence. To prevent parsing issues, you should use double quotes to escape the sentence text and two double quotes (\"\") for double quotes within a sentence. Note that test.csv is a valid submission file itself.</p>\r\n<p>The file should contain a header and have the following format:</p>\r\n<pre>id,\"sentence\"<br />1,\"Former Dodgers manager , the team 's undisputed top ambassador , is going strong at 83 and serving up one great story after another .\"<br />2,\"8 parliamentary elections meant to restore democracy in this nuclear armed nation , a key ally against Islamic .\"<br />3,\"Sales of drink are growing 37 per cent month-on-month from a small base .\"<br />etc...</pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>\r\n        Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.<br/>\r\n    </p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 5 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>5/8/2014 8:10 PM UTC</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>5/1/2015 11:59 PM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<ul>\r\n<li>Due to the public nature of the data, this competition does not count towards Kaggle ranking points.</li>\r\n<li>You may only use the provided training data to train your model.</li>\r\n<li>We ask that you respect the spirit of the competition and do not cheat. You should not submit entries based on test-set answers or train your model on the test set. Hand labeling is also forbidden.</li>\r\n</ul>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}