  A naive likelihood ratio (LR) estimation using the observed frequencies of
events can overestimate LRs for infrequent data. One approach to avoid this
problem is to use a frequency threshold and set the estimates to zero for
frequencies below the threshold. This approach eliminates the computation of
some estimates, thereby making practical tasks using LRs more efficient.
However, it still overestimates LRs for low frequencies near the threshold.
This study proposes a conservative estimator for low frequencies, slightly
above the threshold. Our experiment used LRs to predict the occurrence contexts
of named entities from a corpus. The experimental results demonstrate that our
estimator improves the prediction accuracy while maintaining efficiency in the
context prediction task.
