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Incorporating Structured Commonsense Knowledge in Story Completion
The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant
Local partial likelihood estimation in proportional hazards regression
Fan, Gijbels and King [Ann. Statist. 25 (1997) 1661--1690] considered the
estimation of the risk function in the proportional hazards model.
Their proposed estimator is based on integrating the estimated derivative
function obtained through a local version of the partial likelihood. They
proved the large sample properties of the derivative function, but the large
sample properties of the estimator for the risk function itself were not
established. In this paper, we consider direct estimation of the relative risk
function for any location normalization point .
The main novelty in our approach is that we select observations in shrinking
neighborhoods of both and when constructing a local version of the
partial likelihood, whereas Fan, Gijbels and King [Ann. Statist. 25 (1997)
1661--1690] only concentrated on a single neighborhood, resulting in the
cancellation of the risk function in the local likelihood function. The
asymptotic properties of our estimator are rigorously established and the
variance of the estimator is easily estimated. The idea behind our approach is
extended to estimate the differences between groups. A simulation study is
carried out.Comment: Published at http://dx.doi.org/10.1214/009053606000001299 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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