1 / 9

Automatic Semantic Role Tagger for Verb Clustering

A classification-based, probabilistic model integrating constituent-based methodology by Daniel Gildea & Martha Palmer in ACL 2002. Current implementation involves Multi-class SVM using libsvm package from CSIE, NTU with a radial basis kernel at degree 3. Feature sets include verb, phrasal type, path, voice, position, and sub_cat frame. Challenges include model selection and the trade-off between lexical richness and data sparseness in clustering slot-verb role nouns. Verb clustering utilizes a revised model of Gildea 2002. Future work involves automatic constituent boundary detection, adding more features, and exploring other machine learning models.

ursula-melton
Download Presentation

Automatic Semantic Role Tagger for Verb Clustering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Automatic Semantic Role Tagger Szu-Ting Yi

  2. Overview • Classification-based • Probabilistic model • Constituent-based

  3. Current Implementation • Daniel Gildea & Martha Palmer (ACL 2002) • Multi-class SVM (using libsvm package from CSIE, NTU) • Kernel: radial basis • Degree: 3

  4. Feature Sets • Verb – the predicate • Phrasal type • Path • Voice • Position • Sub_cat frame

  5. Results

  6. Problems • Model selection • Trade-off between more lexical information & data sparseness

  7. cluster slot verb role noun Verb Clustering • (revised model of Gildea 2002)

  8. Results

  9. Conclusion & Future work • Automatic constituent boundary detection • Incorporating more features • Other machine learning models

More Related