To appear in ACL’16 Two Discourse Driven Language Models for Semantics
Table 5: Co-reference resolution results with entity-centered SemLM features. “EC” stands for the entity-centered SemLM. “TRI” is the tri-gram model while “LB” is the log-bilinear model. “pc” means conditional probability features and “em” represents frame embedding features. “w/o DIS” indicates the ablation study by removing all discourse makers for SemLMs. We conduct the experiments by adding SemLM features into the base system. We outperform the state-of-art system [Wiseman et al.2015], which reports the best results on CoNLL12 dataset. The improvement achieved by “EC_LB (pc+em)” over the base system is statistically significant.
['[EMPTY]', 'ACE04', 'CoNLL12']
Tri-grams with conditional probability features improve the performance by a small margin, while the log-bilinear model achieves a 0.4-0.5 F1 points improvement. In addition, we carry out ablation studies to remove all discourse makers during the language modeling process. We re-train our models and study their effects on the generated features.