Lexicalisation is one of the most challenging tasks of Natural Language Generation (NLG). This paper presents our work in choosing between long and short forms of elastic words in Chinese, which is a key aspect of lexicalisation. Long and short forms is a highly frequent linguistic phenomenon in Chinese such as -(laohu-hu, tiger). The choice of long and short form task aims to properly choose between long and short form for a given context to producing high-quality Chinese.We tackle long and short form choice as a word prediction question with neural network language modeling approaches because of their powerful language representation capability. In this work, long and short form choice models based on the-state-of-art Neural Network Language Models (NNLMs) have been built, and a classical n-gram Language Model (LM) is constructed as a baseline system. A well-designed test set is constructed to evaluate our models, and results show that NNLMs-based models achieve significantly improved performance than the baseline system.