Improving Topic Evaluation Using Conceptual Knowledge
The growing number of statistical topic models led to the need to better evaluate their output. Traditional evaluation means estimate the model's fitness to unseen data. It has recently been proven than the output of human judgment can greatly differ from these measures. Thus the need for methods that better emulate human judgment is stringent. In this paper we present a system that computes the conceptual relevance of individual topics from a given model on the basis of information drawn from...[Show more]
|Collections||ANU Research Publications|
|Source:||On Qualitative Route Descriptions: Representation and Computational Complexity|
|Access Rights:||Open Access|
|01_Musat_Improving_Topic_Evaluation_2011.pdf||696.32 kB||Adobe PDF|
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