In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to reﬁne it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application.
We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classiﬁcation problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem. To demonstrate the applicability of the algorithms, we evaluated them on a number of classiﬁcation tasks related to incremental dependency parsing. ...
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