WOE can run in two modes: a CRF extractor (WOEPOS) trained with shallow features like POS tags; a pattern classfier (WOEparse) learned from dependency path patterns.

We show that abstract dependency paths are a highly informative feature when performing unlexicalized extraction.

(Snow et al., 2005) utilize WordNet to learn dependency path patterns for extracting the hypernym relation from text.

However, our results imply that abstracted dependency path features are highly informative for open IE.

WOEparse uses a pattern learner to classify whether the shortest dependency path between two noun phrases indicates a semantic relation.

Despite some evidence that parser-based features have limited utility in IE (Jiang and Zhai, 2007), we hoped dependency paths would improve precision on long sentences.

Shortest Dependency Path as Relation: Unless otherwise noted, WOE uses the Stanford Parser to create dependencies in the “collapsedDepen-dency” format.

We therefore mapped the DIRT Inference rules (Lin and Pantel, 2001), (which consist of pairs of dependency paths ) to TEXTRUNNER relations as follows.

From the parses we extracted all dependency paths between nouns that contain only words present in the TEXTRUNNER relation string.

These dependency paths were then matched against each pair in the DIRT database, and all pairs of associated relations were collected producing about 26,000 inference rules.