This paper is concerned with the problem of learning and exploiting string patterns in natural language processing, particularly information extraction. We propose a new algorithm for learning such patterns. Our algorithm is novel in that it can learn non-consecutive patterns with constraints , which are necessary for information extraction. Specifically, it employs an extended version of the so-called apriori algorithm at the pattern generation step. Our experimental results indicate that in information extraction the use of non-consecutive patterns with constraints is significantly better than the use of only consecutive patterns.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper is concerned with the problem of learning and exploiting string patterns in natural language processing, particularly information extraction. We propose a new algorithm for learning such patterns. Our algorithm is novel in that it can learn non-consecutive patterns with constraints , which are necessary for information extraction. 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