Construction of a CorpusBased Metaphor Generation Support System Built on Japanese Literature Metaphor generation is influenced by emotional or sensuous knowledge structure. Hence, there is a possibility that it might be difficult to select an adequate vehicle in metaphor generation. The purpose of this research is to construct a metaphor database based on a literature corpus and use the database to build a metaphor generation support system. At first, sentences including metaphors were extracted from a literature corpus. The extracted sentences were analyzed using dependency parsing in order to construct a metaphor database for the metaphor generation system. The system outputs candidate vehicles from a given topic and its expressed features by searching the sentences including the topic or the features in the database. Furthermore, an experiment was conducted to evaluate the usability of the system. In the experiment, participants were asked to generate a sentence including a metaphor from a shown image with or without the system. Thirdparty evaluation was conducted to evaluate the metaphors generated in the experiment. The results seem to suggest the efficiency of the system.

Rhyming KnowledgeAware Deep Neural Network for Chinese Poetry Generation Analyzing and capturing the spirit in the historic Tang Dynasty poems for creating a machine that can compose new poetry is a difficult but fun challenge. In this research, we propose a rhyming knowledgeaware deep neural network for Chinese poetry generation. The model fuses rhyming knowledge that represents phonological tones into a long shortterm memory (LSTM) model. This work will help us understand more about what kind of mechanism within the neural network contributes to different styles of the generated poems. The experimental results demonstrate that the proposed method is able to guide the style of those poems towards higher phonological compliance, fluency, coherence, and meaningfulness, as evaluated by human experts. We believe that future research can adopt our approach to further integrate more knowledge such as sentiments, POS, and even stylistic patterns found in poems by famous poets into poem generation.