Cluster-based and brute-correcting grammatical rules learning

In this paper, we propose a cluster-based and brute-correcting grammatical rules learning method which is based on some conclusions of the cognitive linguistics. First, instances of grammatical category are mapped to graphic vectors and distance between two vectors is defined. The set of vectors and the defined distance are proved to form a distance space. Next, this space is mapped to Euclidean space and a simple clustering algorithm is applied to acquire clusters. Then, grammatical rules are learned to describe the cluster. Finally, brute-correcting progress helps to refine the rules. After describing the method we compare the brute-correcting progress with Eric Brill´s transformation-based learning approach [E. Brill, 1995] informally and present an application in Chinese named entity recognition.