during the morning:
reading slides about srl : www.denizyuret.com/ref/yih/SRL-Tutorial-hlt-naacl-06.pdf

2pm to 4pm
example of srl
atach labels (syntactic and semantics)
shared task -> they defined the labels

corpora:
*propbank
*verbNet
*frame net

explain pruning
identification argument
labeling

predicate word: select the sense of the word

data formats:
1- constituent structure tree 2005
2- dependenci strutture 2009




SYSTEMS:

• tree distance -> hector
  
• tree kernel -> liliana
  
• graph matching
• conventionals: max entropy, conditional random fields
  

error analysis -> Gerard.




next week:
presentation on svm & knn


framenet is a dictionary
propbank is a corpus


every one should see the work of the others

OUTPUT of the directed studies:
c++ system
presentation
report
produce new ideas


home work:
basic statistics:
how often appear A0
make a better description list of what has been done.

chatting about how to design the SRL system.
2 implementations

two hours meeting on semantic role labeling research group

we decided to work on semantic role labeling

sematic role labeling

2h meeting

sematic role labeling:

how to predict the semantic role relation:

predict the name of the relation.

predict the relation it-selve

1.- tree distance knn

matching with the k neigbours the best fit.

2.- three kernels:

analizing how looks each posible subtree as a target

CoNLL-2009

sematic role labeling meeting 3h

ILT3 Meeting 3hours.

talk with martin c++ 

meeting with martin.

meeting with martin.

long explanation of kNN implementation algorithm,

dudani vote method  -> depend on the distance of the most fard point

the size of k (knn) can change if are several samples at the same distance.

our dataset is base on trees distance (there are not vectors), 

is about question answering, the original data are questions, and the labels are about what is looking for.

all is implemented in c++ 

QBank Manager class recover the original question.