{
  "fetched_at": "2026-10-19T21:51:07Z",
  "source_url": "https://www.kaggle.com/competitions/bbm497-2020-assignment2/data",
  "pages": [
    {
      "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!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "The dataset is a CoNLL 2003 NER dataset that is composed of sentences with wordsper  line  5  possible  labels  :   “LOC,  ORG,  MISC,  PER,  O”,  that  represent  the  NERclasses.   The  “LOC,  ORG,  MISC,  PER,  O”  labels  are  abbreviation  of  “LOCATION,ORGANIZAT ̇ION, MISCELLANEOUS, PERSON, OTHER” respectively.  T",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "Named-entity  recognition  (NER)  is  a  subtask  of  information  extraction  that  seeks  tolocate and classify named entity mentioned in unstructured text into pre-defined cate-gories such as person names, organizations, locations, medical codes, time expressions,quantities, monetary values, percentages, etc.\n\n**Example:** An example tagging:\n\nEU rejects German call to boycott British lamb .\n\nEU/B-ORG rejects/O German/B-MISC call/O to/O boycott/O British/B-MISC lamb/O ./O\n\n\nIn this assignment, you will implement a NER tagger using Hidden Markov Models\n(HMMs). Therefore, you will practice HMMs and Viterbi algorithm in this assignment.\n\n## CHALLENGE\n\nYou can improve your NER tagger more to compete with other participants in the class. The accuracy of your NER tagger is the main criteria to be compared with others. The participants in the class that obtain a higher tagging accuracy will deserve more points. ",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "## Evaluation\n\nYour program will compute the Accuracy of the PoS tagger.\n\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/accuracy\"> Accuracy</a>. The accuracy is the ratio\nof the correctly assigned tags to the total number of words in the test data:</p>\n<p>\\[ A(W) =  \\frac{\\#of\\_correct\\_found\\_tags}{\\#of\\_total\\_words}\\]</p>\n\n<h2>Submission Format</h2>\n<p> Submission file should contain <strong>sentences with pos tag sequences</strong>.\n\n<pre>EU/B-ORG rejects/O German/B-MISC call/O to/O boycott/O British/B-MISC lamb/O ./O<br></pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}