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A transformation-based approach to argument labeling

A transformation-based approach to argument labeling. Derrick Higgins Educational Testing Service dhiggins@ets.org. General approach. Word-by-word SRL Modified IOB scheme for indicating role boundaries Start from simplistic baseline labeling

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A transformation-based approach to argument labeling

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  1. A transformation-based approach to argument labeling Derrick Higgins Educational Testing Service dhiggins@ets.org

  2. General approach • Word-by-word SRL • Modified IOB scheme for indicating role boundaries • Start from simplistic baseline labeling • TBL rules re-label words based on contextual features

  3. Data representation Modified IOB

  4. Features • Fairly standard set; role label of word depends on: • Target verb • Target verb POS • Target verb passive • Word • POS • Chunk tag • NE tag • L/R of target word • Clause embedding • PP feature • PP head • NP head • Path • Values for current word and surrounding words • No use made of PB frames

  5. Transformational rules • 130 total • Minimum number of applications = 3 • (Mostly) local rules • Local syntactic features + [path, target V, NP head, etc] • Rules using context • Smoothing rules • Long-distance rules

  6. Results • Overtraining is an issue • Core arguments easier than modifiers

  7. Results

  8. Results

  9. Error analysis • Pros/cons of TBL • Pro: easy conditioning on many factors • Con: Little control over trade-off between rule frequency and rule type in selecting rules • Con: Predictive features which are correlated with one another may not be used jointly • Con: No real probabilistic framework • Problems with low-freq. roles

  10. Error analysis • Dependency on length

  11. Potential improvements • Phrase-by-phrase labeling • Using ‘official’ baseline • Rules in ordered sets? • Global optimization • Additional features

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