{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mining-interesting-trivia-for-entities-from","title":"Mining Interesting Trivia for Entities from Wikipedia","arxiv_id":"1510.03025","date":"2015-10-11","proceeding":null,"authors":["Prakash Abhay"],"abstract":"Trivia is any fact about an entity, which is interesting due to any of the\nfollowing characteristics - unusualness, uniqueness, unexpectedness or\nweirdness. Such interesting facts are provided in 'Did You Know?' section at\nmany places. Although trivia are facts of little importance to be known, but we\nhave presented their usage in user engagement purpose. Such fun facts generally\nspark intrigue and draws user to engage more with the entity, thereby promoting\nrepeated engagement. The thesis has cited some case studies, which show the\nsignificant impact of using trivia for increasing user engagement or for wide\npublicity of the product/service.\n  In this thesis, we propose a novel approach for mining entity trivia from\ntheir Wikipedia pages. Given an entity, our system extracts relevant sentences\nfrom its Wikipedia page and produces a list of sentences ranked based on their\ninterestingness as trivia. At the heart of our system lies an interestingness\nranker which learns the notion of interestingness, through a rich set of\ndomain-independent linguistic and entity based features. Our ranking model is\ntrained by leveraging existing user-generated trivia data available on the Web\ninstead of creating new labeled data for movie domain. For other domains like\nsports, celebrities, countries etc. labeled data would have to be created as\ndescribed in thesis. We evaluated our system on movies domain and celebrity\ndomain, and observed that the system performs significantly better than the\ndefined baselines. A thorough qualitative analysis of the results revealed that\nour engineered rich set of features indeed help in surfacing interesting trivia\nin the top ranks.","url_abs":"http://arxiv.org/abs/1510.03025v1","url_pdf":"http://arxiv.org/pdf/1510.03025v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mining-interesting-trivia-for-entities-from","repo_url":"https://github.com/abhayprakash/WikipediaTriviaMiner_SharedResources","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}