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Robust Semantic Role Labeling for Nominals

Developed system for Nominal Semantic Role Labeling, improving SOTA with syntactic context and animacy features, tested on NomBank corpus. Significantly boosts results over unseen predicate/constituents. Achieved +0.12 FB1 over all NomBank.

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Robust Semantic Role Labeling for Nominals

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  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 [thepoliceARG0?/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: Us! Liu & Ng, 2007 (Baseline)

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

  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

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