The impact of enriched linguistic annotation on the performance of extracting relation triples

A relation extraction system recognises pre-defined relation types between two identified entities from natural language documents. It is important for a task of automatically locating missing instances in knowledge base where the instance is represented as a triple (‘entity – relation – entity’). A relation entry specifies a set of rules associated with the syntactic and semantic conditions under which appropriate relations would be extracted. Manually creating such rules requires knowledge from information experts and moreover, it is a time-consuming and error-prone task when the input sentences have little consistency in terms of structures and vocabularies. In this paper, we present an approach for applying a symbolic learning algorithm to sentences in order to automatically induce the extraction rules which then successfully classify a new sentence. The proposed approach takes into account semantic attributes (e.g., semantically close words) as well as linguistic features(entity types) in generalising common patterns among the sentences which enable the system to cope better with syntactically different but semantically similar sentences. Not only does this increase the number of relations extracted, but it also improves the accuracy in extracting relations by adding features which might not be discovered only with syntactic analysis. Experimental results show that this approach is effective on the sentences of the Web documents obtaining 17% higher precision and 34% higher recall values