Proximity Window Context Method for Term Extraction in Ontology Learning from Text

The ontology learning from text cycle consists of the consecutive phases of term, synonym, concept, taxonomy and relation extraction. The paper touches the problems of a low efficiency in the current term extraction methods which are handled by a combination of statistic (frequency-based) and linguistic approaches. We present a novel method to extract terms that uses only shallow linguistic information. It is proposed to explore a different set of linguistic layers and support a classic POS n-gram model with additional context information based on proximity window features. The method is evaluated on two substantially different corpora to produce better results than the classic measures, including standard n-gram models and frequency-based approaches.