Efficient and accurate natural language analysis with lookahead of analysis actions

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We have developed a novel machine learning algorithm that can be used for various natural language processing tasks such as part-of-speech tagging and parsing. The algorithm enables us to incorporate a look-ahead mechanism into a history-based model and significantly improve its accuracy. Experimental results demonstrate that our approach outperforms conditional random field models, which are currently the standard approach in the field, in several natural language processing tasks.