Properties can be mapped to features in a machine learning algorithm in different ways, potentially yielding different results. In previous work, we experimented with various approaches to organizing collocational properties into features in a probabilistic classifier. It was found that one type of organization in particular, which is rarely used in NLP, allows one to take advantage of infrequent but high quality properties for an abstract discourse interpretation task. Based on an analysis of the experimental results, this paper suggests criteria for recognizing beneficial ways to include collocational information in probabilistic classifiers.
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