The standard process for pattem-based relation extraction is to start with hand-selected patterns or word pairs expressing a particular relationship, and iteratively scan the corpus for co-appearances of word pairs in patterns and for patterns that contain known word pairs .

We also propose a way to label each cluster by word pairs that represent it best.

(Alfonseca et al., 2006) for extracting general relations starting from given seed word pairs .

Unlike most previous work, our hook words are not provided in advance but selected randomly; the goal in those papers is to discover relationships between given word pairs , while we use hook words in order to discover relationships that generally occur in the corpus.

To label pattern clusters we define a HITS measure that reflects the affinity of a given word pair to a given cluster.

Several recent papers discovered relations on the web using seed patterns (Pantel et al., 2004), rules (Etzioni et al., 2004), and word pairs (Pasca et al., 2006; Alfonseca et al., 2006).

We addressed the evaluation questions above using a SAT-like analogy test automatically generated from word pairs captured by our clusters (see below in this section).

The header of the question is a word pair that is one of the label pairs of the cluster.

In our sample there were no word pairs assigned as labels to more than one cluster4.

To enrich the set of given word pairs and patterns as described in Section 4.1 and to perform clarifying queries, we utilize the Yahoo API for web queries.

If only several links were found for a given word pair we perform local crawling to depth 3 in an attempt to discover more instances.

The standard classification process is to find in an auxiliary corpus a set of patterns in which a given training word pair co-appears, and use pattern-word pair co-appearance statistics as features for machine learning algorithms.

Co-appearance of nominal pairs can be very rare (in fact, some word pairs in the Task 4 set co-appear only once in Yahoo web search).