A Common Sense Inference Model Based On Natural Language And Memory Reconsolidation

The combination of natural language processing (NLP) and inference egine has play a more and more important role in the pass decay in many application fields such as search engins, data collections in bioinformatics and so on. Researchers have been using statistic supervised and semi-supervised machine learning methods with tagged data to train efficient NLP models. However, with the ever increasing text data, it becomes an impossible task to tag the data in all fields only by hand. Thus people have to refine the selected materials with their own knowledge. On the other hand, inference engins that can be inbedded in to expert systems can logically collect useful data in one field and applied it to another filed. This has suggested that it could be a decent way to process the unlimited data by using inference engine to extend the knowledge in one field to another.In this essay, NLP technique and inference engin are combined to simulate the recognition process of text data by human brain. With the theory of memory reconsolidation, the text-based inference system can analize descriptive sentences and give feedback on the input.The main contribution of this essay includes:1) Propose a mechanism for word sense disambiguation. The word dictionary WordNet and VerbNet are used to provide data to the system to select the right meaning of a word.2) Propose the extended sematic network for a memory model to store refined text data. The extended semantic network outperform the traditional semantic network on storing complicated text and make sure the memory reconsolidation process can be perform smoothly.3) Construct a data-auto- transferring framework for text store model and inference model.4) Select, create and combine beyesian network from knowledge base automatically to make inference on different scenarios.5) Simulate a memory reconsolidation process to improve the inference on limited data.