Selecting Optimal Feature Template Subset for CRFs

Conditional Random Fields (CRFs) are the state-of-the-art models for sequential labe-ling problems. A critical step is to select optimal feature template subset before em-ploying CRFs, which is a tedious task. To improve the efficienc y of t his step, we pro-pose a new method that adopts the maxi-mum entropy (ME) model and maximum entropy Markov models (MEMMs) instead of CRFs considering the homology be-tween ME, MEMMs, and CRFs. Moreover, empirical studies on the efficiency and ef-fectiveness of the method are conducted in the field of Chinese text chunking, whose performance is ranked the first place in task two of CIPS-ParsEval-2009.