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Creator |
20fbc73bc6c0afc98372cf072a54acc4 |
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Creator |
2482a533b100c51b082644502f2b86e0 |
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Creator |
6a5620b7e8134436ca39e792b4432945 |
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Creator |
0ab97f84b0b21f6aab20f88012180fe1 |
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Creator |
ext-168d529095a262289df68cb7419a6aa3 |
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Date |
2014-06-23 |
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Is Part Of |
repository |
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abstract |
This paper addresses the problem of determining the best answer in Community-based
Question Answering websites by focussing on the content. Previous research on this
topic relies on the exploitation of community feedback on the answers, which involves
rating of either users (e.g., reputation) or answers (e.g. scores manually assigned
to answers). We propose a new technique that leverages the content/textual features
of answers in a novel way. Our approach delivers better results than related linguistics-based
solutions and manages to match rating-based approaches. More specifically, the gain
in performance is achieved by rendering the values of these features into a discretised
form. We also show how our technique manages to deliver equally good results in real-time
settings, as opposed to having to rely on information not always readily available,
such as user ratings and answer scores. We ran an evaluation on 21 StackExchange websites
covering around 4 million questions and more than 8 million answers. We obtain 84%
average precision and 70% recall, which shows that our technique is robust, effective,
and widely applicable. |
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authorList |
authors |
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presentedAt |
ext-b7b2b7098400752b24e012d49c832895 |
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status |
peerReviewed |
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type |
AcademicArticle |
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type |
Article |
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label |
Gkotsis, George ; Stepanyan, Karen ; Pedrinaci, Carlos ; Domingue, John and Liakata,
Maria (2014). It's all in the content: state of the art best answer prediction based
on discretisation of shallow linguistic features. In: ACM Web Science, 23-26 Jun
2014, Bloomington, Indiana, USA, pp. 202–210. |
59244 |
label |
Gkotsis, George ; Stepanyan, Karen ; Pedrinaci, Carlos ; Domingue, John and Liakata,
Maria (2014). It's all in the content: state of the art best answer prediction based
on discretisation of shallow linguistic features. In: ACM Web Science, 23-26 Jun
2014, Bloomington, Indiana, USA, pp. 202–210. |
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Title |
It's all in the content: state of the art best answer prediction based on discretisation
of shallow linguistic features |
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in dataset |
oro |