Chinese Semantic Parsing For Medicine Field

One of the main goals of natural language processing(NLP)is to build automated systems that can understand and generate human languages.This goal has so far remained elusive.Existing hand-crafted systems can provide in-depth analysis of domain sub-languages,but are often notoriously fragile and costly to build.Existing machine-learned systems are considerably more robust,but are limited to relatively shallow NLP tasks.It is difficult for a NLP system to obtain nice performance without semantic parsing.Se-mantic parsing is the task of transforming natural-language sentences into complete,formal,symbolic meaning representations(MR)suitable for reasoning or machine-understanding.In recent years,the research of semantic parsing in English has made great progress.However,lit-tle work has been done in Chinese semantic parsing.The medical field has a rich source of data,which is delivered by text.It is difficult for computer to process and understand the medical data.In this thesis,we propose a statistical approach called CSPMF(Chinese Semantic Parsing for Medicine Field)aiming at Chinese semantic parsing for medicine text,which consider the process of converting Chinese sentence into its corresponding meaning as a machine translation procedure.At first,we create a new dataset of medicine for Chinese Semantic Parsing,in which each data contains a Chinese sentence and its accurate meaning.Then we use the word align-ment model to acquire the bilingual dictionary made up by the Chinese natural language string and its meaning.In the end,we determine the ultimate semantic analysis by learning a statis-tical model.Considering the grammatical structure of Chinese,we also propose an improved approach called ICSPMF(Improved Chinese Semantic Parsing for Medicine Field,ICSPMF),ICSPMF uses preprocessing algorithm to preprocess dataset.Experiments show that CSPMF performs well with higher precision and recall.