What Did We See?& WikiGIS

Chris PalUniversity of Massachusetts

A Talk for Memex DayMSR Redmond, July 19, 2006

Research Questions

1. How do personal and community photo-journals and blogs interact?Spectrum from personal blogs – community portals (bliki’s) – Wiki articles (most public) User Interface & Social Computing Research

2. Can we ‘mine’ information in Blogs ?Find Blog entries that look like Wiki entries, extract information, encourage contributions?Document and Text Processing Research

3. What is the role of computer vision for location and object recognition?Can we use these methods to provide the user with relevant information?

Search Blogs and Wiki Entries

Questions About Observations

Search and Social Computing

I Discover that my friend Justin also found an interesting mushroom

Have I been here as well?

1. Object RecognitionFrom Images and Text

2. Location RecognitionFrom Images and Text

Object and Location Recognition

Conditional Random Fields

yt -1

yt

xt

yt+1

xt +1

xt -1

. . .

yt+2

xt +2

yt+3

xt +3

said Ling a Microsoft VP …

OTHER PERSON OTHER ORG TITLE …

Named Entities

(SFSM states)

Binary Features

Input Sequence

• Widely applicable, many positive results e.g. speech recognition

• Fact Extraction (from Blogs and Wikis)

• Address extraction

• Information Extraction Example

Research Result - Training a CRF

• Define the vector of feature values a time t

• Define the global feature function as

• The gradient of the conditional log likelihood

Model expectation, i.e.Empirical expectation

Results: CRF Training

NetTalk text-to-speech: Linear-chain CRF training using sparse inference

75% less training time than exact training, with no loss in accuracy

Accuracy:

Fixed: 85.7KL: 91.6Exact: 91.6

SenseCam Enhanced Blogs

Produce Lots of Data for Location Recognition

Multi-Conditional Learning

• Motivation - Simple GMM Example Joint Conditional Multi-Conditional

Multi-Conditional Learning

• One motivation: Conditional Random Fields can be derived from a traditional joint model

• But, there are many other conditional distributions that could be defined

• What do we gain if we model those as well?

• Other combinations possible

Image Segmentation/Pixel Classification

MSR Cambridge / Berkeley Data

Mixtures of Factor Analyzers

• Generative model for simultaneous dimensionality reduction and clustering

• We wish to obtain a discriminative version of this type of model discriminatively

Performance vs. Model Complexity

Interesting ?

Joint Optimization benefits more substantially from additional data.

Performance with More DataTraining Set Accuracy Test Set Accuracy

hmm…

Search Blogs of Friends

Detect and Find Expert Knowledge

Simple Exponential Family Models for Documents

Results: Document Classification

New Graphical Models for Email and Blogs

xb

y

Nb

xsNs

xrNr-1

Body Title FriendsWords Words discussed

PredictedRecipient

Nr

- function - random variable

- N replicationsN

Email Model: Nb words in the body, Ns words in the subject, Nr recipients

The graph describes the joint distribution of random variables in term of the product of local functions

• Scenario: Predict which friends might be interested in your new Blog entry

• New Idea: Plated Factor Graphs

Detect Quality Content and Encourage Knowledge Contributions

Conclusions, Present & Future Work

• WikiGIS – Merged Blogs, Blikis and Wikis with Microsoft Virtual Earth

• Merge the SenseCam with a smart Phone- Enable Intelligent Digital Assistants - Output to the television

• Next Steps: Location and object recognition enabling information retrieval

• Other Uses: Assistive Technology for the Elderly

References & Results so Far

• with Charles Sutton and Andrew McCallum. Sparse Forward-Backward using Minimum Divergence Beams for Fast Training of Conditional Random Fields. In proceedings of ICASSP 2006.

• with Michael Kelm and Andrew McCallum. Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning To appear in the proceedings of ICPR 2006.

• with Andrew McCallum, Greg Druck and Xuerui Wang. Multi-Conditional Learning: Generative/ Discriminative Training for Clustering and Classification To appear in the proceedings of AAAI 2006.

• CC Prediction with graphical models To appear in the proceedings of CEAS 2006.