This paper aims to develop a framework for automatic CG tagging. We investigated two main algorithms, CRF and Statistical alignment model based on information theory (SAM). We found that SAM gives the best results both in word level and sentence level. We got the accuracy 89.25% in word level and 82.49% in sentence level. Combining both methods can be suited for both known and unknown word.

Combinatorial Category Grammar is (CCG) a lexicalized grammar formalism which is expressed by syntactic category, a logical form representation. There are difficulties in representing CCG without any visualization tools. This paper presents a design framework of OpenCCG workbench and visualization tool which enables linguists to develop CCG based lexicons more easily. Our research is aimed to resolve these gaps by developing a user-friendly tool. OpenCCG Workbench, an open source web-based environment, was developed to enable multiple users to visually create and update grammars for using with the OpenCCG library. It was designed to streamline and speed-up the lexicon building process, and to free the linguists from writing XML files which is both cumbersome and error-prone. The system consists of three sub-systems: grammar management system, grammar validator system, and concordance retrieval system. In this paper we will mainly discuss the most important parts, grammar management and validation systems, which are directly related to a CCG lexicon construction. We support users in three levels; Expert linguists who play a role as lexical entry designer, normal linguists who adds or edits lexicons, and guests who requires an acquisition to the lexicon into their applications.