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A Memory-Based Approach to Semantic Role Labeling

Beata Kouchnir Tübingen University. A Memory-Based Approach to Semantic Role Labeling. 05/07/04. Introduction. Applying Memory-Based Learning to the task of Semantic Role Labeling (using the TiMBL software)

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A Memory-Based Approach to Semantic Role Labeling

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  1. Beata Kouchnir Tübingen University A Memory-Based Approach to Semantic Role Labeling 05/07/04

  2. Introduction • Applying Memory-Based Learning to the task of Semantic Role Labeling (using the TiMBL software) • Data is processed by chunks, except for the target verb chunks, which can contain modals and negation • Task is split into two modules: • Recognition: identifies the arguments of a target verb • Labeling: assigns a semantic role to each argument Beata Kouchnir 05/07/04 1

  3. Memory-Based Learning • Training instances are stored without abstraction • Test instances are assigned the most frequent class within a set of k most similar examples (k-nearest neighbors) • Similarity is computed based on a distance metric: • Overlap: 1 if two values are the same, 0 otherwise (for symbolic values) • Modified value difference: determines similarity based on co-occurrence of values with classes Beata Kouchnir 05/07/04 2

  4. Recognition Features • Head word and POS of the focus element (head is last word of chunk) • Chunk type: one of the 12 chunks types • Position in clause: beginning, end or inside. • Directionality with respect to target verb: before, after, coincides • Numerical distance (1 .. n) to the target verb. • Adjacency to target chunk: adjacent, not adjacent, inside the target chunk Beata Kouchnir 05/07/04 3

  5. Recognition Features (contd.) • Target verb and voice: passive if target verb is a past participle preceded by a form of to be • Context: the features head word, part of speech, chunk type and adjacency of the three chunks each to the left and right of the focus chunk Beata Kouchnir 05/07/04 4

  6. Labeling Features • Word, POS and chunk sequence of the head words of all the chunks in the argument; each sequence represents one value • Clause information: is argument a complete clause? • Length of the argument in chunks • Directionality, adjacency, target verb and voice • Prop Bank roleset of target verb's first sense (86% of targets use first sense) Beata Kouchnir 05/07/04 5

  7. Evaluation • Recognition module: Prec. 53.21%, Rec. 74.97%, F 62.25 • All features improved performance; MVDM, k=7 best parameter setting • Labeling module: Prec. 75.71%, Rec. 74.60%, F 75.15 • POS-sequence and length worsen performance; MVDM, k=1 best parameter setting • Overall development: Prec. 44.93% , Rec. 63.12%, F 52.50 • Overall test: Prec. 56.86%, Rec. 49.95%, F 53.18 Beata Kouchnir 05/07/04 6

  8. Conclusion and Future Work • Recognizing arguments is more difficult than labeling • Removing multiple A0-A5 arguments can increase precision • IOB2 might not be the best representation • Some chunkers can recognize recursive noun phrases • Could improve results without adding too much comlexity • Changing classifier's default parameters considerably improves performance Beata Kouchnir 05/07/04 7

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