Investigating Features for Classifying Noun Relations

Automated recognition of the semantic relationship between two nouns in a sentence is useful for a wide variety of tasks in NLP. Previous approaches have used kernel methods with semantic and lexical evidence for classification. We present a system based on a maximum entropy classifier which also considers both the grammatical dependencies in a sentence and significance information based on the Google Web 1T dataset. We report results comparable with state of the art performance using limited data based on the SemEval 2007 shared task on nominal classification.