Probabilistic Frame InductionThis is a postprint version of a paper to appear in the Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL/HLT 2013).
Table 2: Results on TAC 2010 entity extraction with N-to-1 mapping for N=1 and N=5. Intermediate values of N produce intermediate results, and are not shown for brevity.
['[BOLD] Systems', '[ITALIC] 1-to-1  [ITALIC] P', '[ITALIC] 1-to-1  [ITALIC] R', '[ITALIC] 1-to-1  [ITALIC] F1', '[ITALIC] 5-to-1  [ITALIC] P', '[ITALIC] 5-to-1  [ITALIC] R', '[ITALIC] 5-to-1  [ITALIC] F1']
S5SS0SSS0Px3 Results The overall results are poorer than for the MUC-4 task, but this task is harder given the greater diversity in frames and slots to be induced. Like in the previous evaluation, our system is able to outperform C&J in terms of recall and F1, but not precision. C&J’s method produces many small clusters, which makes it easy to achieve high precision. The N-to-1 mapping procedure can also be seen to favor their method over ours, many small clusters with high precision can be selected to greatly improve recall, which is indeed the case. However, ProFinder with 1-to-1 mapping outperforms C&J even with 5-to-1 mapping.