ILK: Machine learning of semantic relations with shallow features and almost no data
نویسندگان
چکیده
This paper summarizes our approach to the Semeval 2007 shared task on “Classification of Semantic Relations between Nominals”. Our overall strategy is to develop machine-learning classifiers making use of a few easily computable and effective features, selected independently for each classifier in wrapper experiments. We train two types of classifiers for each of the seven relations: with and without any WordNet information.
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تاریخ انتشار 2007