On the Expressiveness of Information Extraction Patterns

Many recently reported machine learning approaches to the acquisition of information extraction (IE) patterns have used dependency trees as the basis for their pattern representations (Yangarber et al., 2000a; Yangarber, 2003; Sudo et al., 2003; Stevenson and Greenwood, 2005). While varying results have been reported for the resulting IE systems little has been reported about the ability of dependency trees, or patterns extracted from them, to represent relationships needed to perform IE. In this paper we evaluate the ability of a number of pattern representations, derived from dependency trees, to represent the relationships being extracted by an IE system. The paper concludes by suggesting the use of the “linked chains” model which represents around 94% of the possible relations in text but without generating an unwieldy number of candidate extraction patterns.