Difference between revisions of "Did You Know?: Mining Interesting Trivia for Entities from Wikipedia"
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| + | | title = Did You Know?: Mining Interesting Trivia for Entities from Wikipedia | ||
| + | | date = 2015 | ||
| + | | authors = [[Abhay Prakash]]<br />[[Manoj Kumar Chinnakotla]]<br />[[Dhaval Patel]]<br />[[Puneet Garg]] | ||
| + | | link = https://dl.acm.org/citation.cfm?id=2832690 | ||
| + | }} | ||
'''Did You Know?: Mining Interesting Trivia for Entities from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Abhay Prakash]], [[Manoj Kumar Chinnakotla]], [[Dhaval Patel]] and [[Puneet Garg]]. | '''Did You Know?: Mining Interesting Trivia for Entities from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Abhay Prakash]], [[Manoj Kumar Chinnakotla]], [[Dhaval Patel]] and [[Puneet Garg]]. | ||
== Overview == | == Overview == | ||
Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness, unexpectedness or weirdness. In this paper, authors propose a novel approach for mining entity trivia from their [[Wikipedia]] pages. Given an entity, system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based [[features]]. Authors ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. Authors evaluated system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that rich set of features indeed help in surfacing interesting trivia in the top ranks. | Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness, unexpectedness or weirdness. In this paper, authors propose a novel approach for mining entity trivia from their [[Wikipedia]] pages. Given an entity, system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based [[features]]. Authors ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. Authors evaluated system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that rich set of features indeed help in surfacing interesting trivia in the top ranks. | ||
Revision as of 11:23, 8 September 2019
Authors | Abhay Prakash Manoj Kumar Chinnakotla Dhaval Patel Puneet Garg |
|---|---|
Publication date | 2015 |
Links | Original |
Did You Know?: Mining Interesting Trivia for Entities from Wikipedia - scientific work related to Wikipedia quality published in 2015, written by Abhay Prakash, Manoj Kumar Chinnakotla, Dhaval Patel and Puneet Garg.
Overview
Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness, unexpectedness or weirdness. In this paper, authors propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based features. Authors ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. Authors evaluated system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that rich set of features indeed help in surfacing interesting trivia in the top ranks.
