Most relation extraction systems identify relations by searching within- sentences (within-sentence relations). Such an approach excludes finding any relations that cross sentence boundaries (cross-sentence relations). This thesis quantifies the cross-sentence relations in two major information ex- traction corpora: ACE03 (9.4%) and MUC6 (27.4%), revealing the extent of this limitation. In response. a composite kernel approach to cross-sentence relation extraction is proposed which models relations using parse tree and fiat surface features. Support vector machine classifiers are trained using cross-sentential relations from the !vIUC6 corpus to determine the effective- ness of this approach. It was shown .that composite kernels are able to extract cross-sentential relations with f-measure scores of 0.512, 0.116 and 0.633 for PerOrg. PerPost and PostOrg models. respectively. Moreover. combining within-sentence and cross-sentence extraction models increases the number of relations correctly identified by 24% over within-sentence relation extraction alone.