'Dependency model' presentation slideshows

New Models for Relational Classification

New Models for Relational Classification. Ricardo Silva (Statslab) Joint work with Wei Chu and Zoubin Ghahramani. The talk. Classification with non-iid data A source of non-iidness: relational information A new family of models, and what is new

667 views • 56 slides

New Models for Relational Classification

New Models for Relational Classification. Ricardo Silva (Statslab) Joint work with Wei Chu and Zoubin Ghahramani. The talk. Classification with non-iid data A source of non-iidness: relational information A new family of models, and what is new

677 views • 56 slides

Mirko Steinle, EPFL and HUG Karl Aberer, EPFL Sarunas Girdzijauskas, EPFL Alexander Lamb, HUG

275 views • 25 slides

Search Engine Statistics Beyond the n-gram: Application to Noun Compound Bracketing CoNLL-2005

Search Engine Statistics Beyond the n-gram: Application to Noun Compound Bracketing CoNLL-2005. Preslav Nakov EECS, Computer Science Division University of California, Berkeley Marti Hearst SIMS University of California, Berkeley. Outline. Introduction Related Work Models and Features.

308 views • 23 slides

CHAPTER 11: DIMENSIONAL MODELING: ADVANCED TOPICS

CHAPTER 11: DIMENSIONAL MODELING: ADVANCED TOPICS. CHAPTER OBJECTIVE: NORMALIZATION THE SNOWFLAKE SCHEMA. Normalization. The process of making your data and tables match these standards is called normalizing data or data normalization.

1.8k views • 22 slides

BUSINESS CONTINUITY PROGRAMME AN INTEGRATED RISK MANAGEMENT APPROACH Bord Gais Eireann Martin Dunlea Chief Information

667 views • 19 slides

Chapter 8

Chapter 8. Uses and Gratifications. Uses and Gratifications Approach. Assumes that differences among audience members cause each person to: Seek out different messages Use those messages differently Respond to them differently. Societal-level Functions of the Mass Media.

311 views • 13 slides

Using Social Annotations to Improve Language Model for Information Retrieval

221 views • 12 slides

An Attempt at Unsupervised Learning of Hierarchical Dependency Parsing

An Attempt at Unsupervised Learning of Hierarchical Dependency Parsing. via the Dependency Model with Valence (DMV). Motivation. Dependency Parsing: Search Query Refinement Statistical Machine Translation Unsupervised Learning: Availability of Large Quantities of Data. DMV.

212 views • 6 slides