An Approach of Chunk Parsing and Entity Relation Extracting to Chinese Based on Conditional Random Fields Model

Abstract

Conditional random fields (CRFs) model is the valid probabilistic model to segment and label sequence data. Comparing with other statistical models, such as HMM, MEHMM, CRFs process the data sequence in terms of the context of data. Chunk analysis is a shallow parsing method to simplify natural language processing. And entity relation extraction is used in establishing relationship between entities. Because full syntax parsing is complexity in Chinese text understanding chunk analysis and relation extraction is important. This paper models these problems to Chinese text. By transforming them into label solution we can use CRFs to realize the chunk analysis and entities relation extraction. In the paper we define the representation of Chinese chunk and entity relation. The features window of the label word is discussed. By training we obtain an optimized CRFs model. It can realize label to chunk and entity relation so as to complete chunk parsing and relation extracting.