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In this paper we propose a new word sense disambiguation method called Multi-engine Collaborative Bootstrapping (MCB) that combines different types of corpora and also uses two languages for bootstrapping. MCB uses the bilingual bootstrapping as its core algorithm that leading to incremental knowledge acquisition. The EM model is applied to train parameters in a base learner. The feature translation model is improved by semantic correlation estimation. In addition we use multi-engine selection to produce qualified starting seeds from parallel corpora and monolingual corpora. Those seeds that are generated through unsupervised machine learning approaches can also ensure bootstrapping effectiveness in contrast with manually selected seeds in spite of their different selection mechanisms. Experimental results prove the effectiveness of MCB. Some factors including feature space and starting seed number are concerned involved in our experiments because the EM algorithm is sensitive to starting values. Limitation of resources is also a concern. [ABSTRACT FROM AUTHOR]

Abstract-Unification grammars are widely used for encoding human knowledge. For unification systems, one major difficulty is the debugging of rules. In this paper, the authors suggest a novel method based on model checking to theoretically verify a complex grammar system for a unification-based parser. We propose the modeling method of the grammar and, more importantly, the abstraction method to compress the state space. We apply partial Kripke structures to model the rules. We prove that the state space can be reduced by several orders of magnitude while still keeping the behaviors of a noncompressed one. Practical verification issues are discussed, including the restrictions on specifications, the properties to check, etc. The proposed method will contribute to the effective debugging and application of unification grammars. [ABSTRACT FROM AUTHOR]