1. Robust Semantic Role Labeling for Nominals Robert Munro Aman Naimat

2. In brief Created a system for Nominal Semantic Role Labeling Useful for Information Extraction and Q&A: An Example The police investigated the crime Agent PRED Patient

3. Architecture Tested on the NomBank corpus (250,000 size) [the crime’s ARG1 ] [investigation PRED ] … [the police’s ARG0 ] [investigation PRED ] … [The investigation PRED ] of [the police ARG0?/ARG1? ] … Based on the current SOTA (Liu & Ng 2007) Developed 12 new features: 1) Syntactic Context: Agents are more likely to be in the sentence’s subject position: 2) Animacy features: The most animate argument is more likely to be the Agent Stanford Classifier (MaxEnt)

4. Our contribution We improved the current State of the Art results: Liu & Ng, 2007 (Baseline) Us!

5. Our contribution Especially over unseen predicate/constituents: Liu & Ng, 2007 (Baseline) Us!

6. Data analysis Syntactic position Animacy

7. Conclusions Features modeling syntactic context and animacy improve nominal-Semantic Role Labeling Consistently outperforms the current state of the art results: +.012 FB1 over all NomBank +.033 FB1 over unseen predicate/constituents Greater improvements are possible