This paper proposes a method for reinforcing noun countability prediction, which plays a crucial role in demarcating correct determiners in machine translation and error detection. The proposed method reinforces countability prediction by introducing a novel heuristics called one countability per discourse. It claims that when a noun appears more than once in a discourse, all instances will share identical countability. The basic idea of the proposed method is that mispredictions can be corrected by efficiently using one countability per discourse heuristics. Experiments show that the proposed method successfully reinforces countability prediction and outperforms other methods used for comparison. In addition to its performance, it has two advantages over earlier methods: (i) it is applicable to any countability prediction method, and (ii) it requires no human intervention to reinforce countability prediction.

An improved arithmetic coding which provides an encoder with finite calculation precision for source sequences over a countable alphabet is presented. Conventional arithmetic coding theoretically has infinite precision for real variables. However any algorithm implemented on a computer has finite precision. This implies that conventional arithmetic codes can only encode sequences over a finite alphabet. The improved arithmetic coding presented here has a computational complexity which is roughly proportional to the length of the source sequence for a given source.