Bayesian Modeling of Dependency Trees Using Hierarchical Pitman-Yor Priors
author:
Hanna M. Wallach,
Department of Computer Science, University of Massachusetts Amherst
You might be experiencing some problems with Your Video player.
| Slides | |
| 0:00 | Bayesian Models for Dependency Parsing Using Pitman-Yor Priors |
| 0:14 | Dependency Parsing - 1 |
| 0:34 | Dependency Parsing - 2 |
| 0:45 | Dependency Parsing - 3 |
| 1:20 | Talk Outline |
| 1:54 | Eisner's Generative Dependency Model - 1 |
| 2:30 | Eisner's Generative Dependency Model - 2 |
| 2:37 | Eisner's Generative Dependency Model - 3 |
| 2:59 | Eisner's Generative Dependency Model - 4 |
| 3:12 | Eisner's Generative Dependency Model - 5 |
| 3:17 | Eisner's Generative Dependency Model - 6 |
| 3:31 | Eisner's Generative Dependency Model - 7 |
| 3:38 | Eisner's Generative Dependency Model - 8 |
| 3:44 | Eisner's Generative Dependency Model - 9 |
| 3:48 | Eisner's Generative Dependency Model - 10 |
| 3:52 | Eisner's Generative Dependency Model - 11 |
| 3:59 | Generating a Tagged, Cased Word - 1 |
| 4:33 | Generating a Tagged, Cased Word - 2 |
| 5:03 | Generating a Tagged, Cased Word - 3 |
| 5:23 | Generating a Tagged, Cased Word - 4 |
| 5:41 | Estimating Probabilities from Data - 1 |
| 7:18 | Estimating Probabilities from Data - 2 |
| 8:43 | Estimating Probabilities from Data - 3 |
| 8:52 | Estimating Probabilities from Data - 4 |
| 9:01 | Estimating Probabilities from Data - 5 |
| 9:13 | Contexts for Tags and Uncased Words |
| 10:00 | A Hierarchical Bayesian Dependency Model |
| 11:31 | Base Measures |
| 12:24 | Predictive Distributions |
| 13:27 | Relationship to Eisner's Model |
| 13:47 | Advantages of the Bayesian Reinterpretation |
| 14:31 | Using Hierarchical Pitman-Yor Priors |
| 15:52 | Pitman-Yor Predictive Distributions |
| 16:37 | Relationship to Bayesian n-gram Language Modeling |
| 17:29 | Using the Model |
| 18:28 | Parsing Experiments |
| 19:24 | Results: Parse Accuracy |
| 20:58 | Latent Variable Parsing Models |
| 22:24 | First Order Models |
| 22:35 | "Syntactic" Latent Variables - 1 |
| 22:57 | "Syntactic" Latent Variables - 2 |
| 23:06 | "Syntactic" Latent Variables - 3 |
| 23:15 | "Syntactic" Latent Variables - 4 |
| 23:20 | "Syntactic" Latent Variables - 5 |
| 23:36 | "Syntactic" Latent Variables - 6 |
| 23:43 | "Syntactic" Latent Variables - 7 |
| 23:51 | "Syntactic" Latent Variables - 8 |
| 23:53 | "Syntactic Topics": Parse Accuracy |
| 25:18 | States Inferred from Treebank Sections 2-21 |
| 26:02 | Unsupervised Leave-One-Out Bits-Per-Word |
| 27:05 | Conclusions and Future Work |
| 27:43 | Questions? |
| 28:16 | - Questions |
Lecture rating
| People found this lecture: | ||
| Worth seeing | ||
| because it is: | ||
| Valuable and informative | ||
| Well presented | ||
| Easily understandable | ||
| Acceptably recorded | ||
| You need to login to cast your vote. | ||
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Related content
Visitors who watched this lecture also watched...
SEE ALSO:
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !





