1. Preliminaries CSCI-GA.2591
NYU
Preliminaries CSCI-GA.2591
Ralph Grishman

2. Goal and Approach
What are the limitations in extracting knowledge from text?
Approach:
start with a skeleton MR system
enhance individual components
enhance ensemble
estimate confidence of components
estimate confidence of combined system
use domain model (Markov Logic Network)
learn as we go

3. Requirements provenance — addressed by using Tipster arch, UIMA
speed - fast enough for rapid development
scaling … develop algorithms of time linear or n log n and use DB-based system (we won't)
capture domain constraints: rule-based inference
capture uncertainty: MLN
enable joint inference: MLN
domain adaptive: emphasize task-specific components

4. Schedule
1. preliminaries (tipster, jet-lite, ACE); sentence segmentation
2. NE
3. coreference
4. XD coreference; brief plan reports
5. relations
6. event
7. time
8. reports on components
9. joint inference: opportunities
10. prob graphical models: beam search, belief prop.
11. Alchemy; domain models
12 KBP systems
13 self-learning
14. project reports

5. ACE
Our system will be designed to read a document and extract the entities, relations, and events
To train and evaluate our system, we need a corpus annotated with this information
We will use the ACE 2005 corpus and the domain of national and international news (very broad)

6. Domains News domain is very broad, hard to model
Difficult to see impact of domain model on language analysis
Time permitting, may use a second, narrower model
football game reports
hurricane news
… suggestions?

7. Corpora ACE 2005: 300 kw Penn Tree Bank Reuters OntoNotes
defines classes of relations and events
widely used benchmark
6 genres
being augmented by ERE annotation
Penn Tree Bank
for sentences and POS
Reuters
for NE annotation
OntoNotes
for coreference and word sense training