MediaWiki API result

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    "warnings": {
        "main": {
            "*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/postorius/lists/mediawiki-api-announce.lists.wikimedia.org/> for notice of API deprecations and breaking changes."
        },
        "revisions": {
            "*": "Because \"rvslots\" was not specified, a legacy format has been used for the output. This format is deprecated, and in the future the new format will always be used."
        }
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    "query": {
        "pages": {
            "34289": {
                "pageid": 34289,
                "ns": 0,
                "title": "Re-Targeted Online Advertisement",
                "revisions": [
                    {
                        "contentformat": "text/x-wiki",
                        "contentmodel": "wikitext",
                        "*": "A [[Re-Targeted Online Advertisement]] is a [[personalized online advertisement]] that is a [[Re-Targeted Advertisement]] (repeats a previously seen ad that showed strong preliminary interest).\n* <B>Example(s):</B>\n** an [[online ad|ad]] to a [[item]] that was previously researched in-detail.\n* <B>See:</B> [[Cold-Start Problem]].\n\n----\n----\n\n== References ==\n\n=== 2012 ===\n* http://en.wikipedia.org/wiki/Behavioral_retargeting\n** '''Behavioral retargeting</B> (also known as '''behavioral remarketing</B>, or simply, '''retargeting</B>) is a form of online [[targeted advertising]] by which [[online advertising]] is targeted to consumers based on their previous Internet actions, in situations where these actions did not result in a sale or [[Conversion (marketing)|conversion]].<ref>{{cite web|url=http://publications.mediapost.com/index.cfm?fuseaction=Articles.showArticleHomePage&art_aid=44145|publisher=Search Insider|title=To Recoup Click-through Losses, Redirect|date=2006-06-05}}</ref>\n<BR>\n* http://en.wikipedia.org/wiki/Personalized_retargeting\n** QUOTE: '''Personalized retargeting</B> is a display advertising technique used by online advertisers to recapture consumers who visit a retailer\u2019s site and leave without making a purchase.  It functions as a complement to search, SEO and other marketing campaign tactics.        <P>             Personalized retargeting, like other forms of retargeting, uses basic information, pulled from [[cookies]] that are placed on a user\u2019s web browser, to serve display advertisements. The products that appear in each user\u2019s display ad are unique to each user and reflect products that the user previously viewed on a retailer\u2019s website.\n<references/>\n\n----\n\n__NOTOC__\n[[Category:Concept]]"
                    }
                ]
            },
            "116742": {
                "pageid": 116742,
                "ns": 0,
                "title": "ReAding Comprehension from Examinations (RACE) Dataset",
                "revisions": [
                    {
                        "contentformat": "text/x-wiki",
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                        "*": "A [[ReAding Comprehension from Examinations (RACE) Dataset]] is a [[reading comprehension dataset]].\n* <B>Context:</B>\n** It can be available online at: <code>http://www.cs.cmu.edu/~glai1/data/race/</code>.\n** \u2026\n* <B>Example(s):</B>\n** that consists of near 28,000 [[passage]]s and near 100,000 [[question]]s generated by [[human expert]]s.\n** \u2026\n* <B>Counter-Example(s):</B>\n** a [[CoQA Dataset]],\n** an [[ImageNet Dataset]],\n** a [[MS COCO Dataset]],\n** a [[SQuAD Dataset]].\n* <B>See:</B> [[Question-Answering System]], [[Natural Language Processing Task]], [[Natural Language Understanding Task]], [[Natural Language Generation Task]].\n\n----\n----\n\n== References ==\n\n=== 2018 ===\n* (Lai, 2018) \u21d2 http://www.cs.cmu.edu/~glai1/data/race/\n** QUOTE: Each passage is a JSON file. The JSON file contains the following fields:\n    article: A string, which is the passage.\n    questions: A string list. Each string is a query. We have two types of questions. First one is an interrogative sentence. Another one has a placeholder, which is represented by _.\n    options: A list of the options list. Each options list contains 4 strings, which are the candidate option.\n    answers: A list contains the golden label of each query.\n    id: Each passage has a unique id in this dataset.\n\n=== 2017 ===\n* ([[2017_RACELargeScaleReAdingComprehens|Lai et al., 2017]]) \u21d2 [[Guokun Lai]], [[Qizhe Xie]], [[Hanxiao Liu]], [[Yiming Yang]], and [[Eduard H. Hovy]]. ([[2017]]). \u201c[https://www.aclweb.org/anthology/D17-1082.pdf RACE: Large-scale ReAding Comprehension Dataset From Examinations]\". In: [[Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)]].\n** QUOTE: We present [[RACE Dataset|RACE, a new dataset]] for [[benchmark evaluation]] of [[method]]s in the [[reading comprehension task]]. </s>[[Collected]] from the [[English exam]]s for middle and [[high school Chinese student]]s in the [[age range]] between 12 to 18, [[RACE]] consists of near 28, 000 [[passage]]s and near 100, 000 [[question]]s generated by [[human expert]]s ([[English instructor]]s), and covers a variety of [[topic]]s which are carefully designed for [[evaluating]] the student's ability in [[understanding]] and [[reasoning]]. </s>\n\n----\n\n__NOTOC__\n[[Category:Concept]]\n[[Category:Machine Learning]]\n[[Category:Computational Linguistics]]"
                    }
                ]
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