While it is possible to train these parameters via direct annotation of messages with label sequences, we opted instead to use a simple approach where message tokens from the training weekend are labeled via their intersection with gold records, often called “distant supervision” (Mintz et al., 2009b).

The weights of the CRF component of our model, QSEQ, are the only weights learned at training time, using a distant supervision process described in Section 6.

Our work also relates to recent approaches for relation extraction with distant supervision (Mintz et al., 2009b; Bunescu and Mooney, 2007; Yao et al., 2010a).