Automatic Acquisition of Ranked IS-A Relation from Unstructured Text

In this paper, we present a weakly-supervised, general-purpose algorithm for IS-A relation extraction. The algorithm automatically identifies highly relevant triples for IS-A relation from a text collection and rank the triples through a combination of linguistic and statistical processing. The main features are: i) a method based on dependency structure analysis of texts, ii) a method to exploit domain knowledge based on distributional association of entities, and iii) a iterative and interactive measure of pattern and relation instance reliability. Experimental results show that the algorithm presented here has ranked patterns and instances in some ways preferable. As our approach is not dependent to IS-A relation, we can expand to the extraction of other relation types.