Evaluating Various Linguistic Features on Semantic Relation Extraction

Machine learning approaches for Information Extraction use different types of features to acquire semantically related terms from free text. These features may contain several kinds of linguistic knowledge: from orthographic or lexical to more complex features, like PoStags or syntactic dependencies. In this paper we select four main types of linguistic features and evaluate their performance in a systematic way. Despite the combination of some types of features allows us to improve the fscore of the extraction, we observed that by adjusting the positive and negative ratio of the training examples, we can build high quality classifiers with just a single type of linguistic feature, based on generic lexico-syntactic patterns. Experiments were performed on the Portuguese version of Wikipedia.