Abstract | Most current event extraction systems rely on local information at the phrase or sentence level. |
Conclusion and Future Work | Experiments show that document-level information can improve the performance of a sentence-level baseline event extraction system . |
Cross-event Approach | Our event extraction system is a two-pass system where the sentence-level system is first applied to make decisions based on local information. |
Introduction | Most current event extraction systems are based on phrase or sentence level extraction. |
Introduction | Several recent studies use high-level information to aid local event extraction systems . |
Introduction | We extend these approaches by introducing cross-event information to enhance the performance of multi-event-type extraction systems . |
Related Work | Almost all the current ACE event extraction systems focus on processing one sentence at a time (Grishman et al., 2005; Ahn, 2006; Hardy et al. |
Related Work | They used this technique to augment an information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. |
Experimental Setup | We randomly selected 12 document-image pairs from the test set and generated captions for them using the best extractive system , and two abstractive systems (word-based and phrase-based). |
Results | Table 3 reports mean ratings for the output of the extractive system (based on the KL divergence), the two abstractive systems, and the human-authored gold standard caption. |
Results | It is significantly worse than the phrase-based abstractive system (0c < 0.01), the extractive system (0c < 0.01), and the gold standard (0c < 0.01). |
Results | Unsurprisingly, the phrase-based system is significantly less grammatical than the gold standard and the extractive system , whereas the latter is perceived as equally grammatical as the gold standard (the difference in the means is not significant). |