Skip to content

Our work for the NTCIR-11 Temporalia challenge, Temporal Query Intent Classification sub-task: predicting the temporal orientation of search engine user queries. We tackled the task as a machine learning classification problem, by proposing th use of temporal-oriented attributes specifically designed to minimise the sparsity of the models.

Branch: master
Go to file
Code

Latest commit

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Temporalia-TQIC

Our work for the NTCIR-11 Temporalia challenge, Temporal Query Intent Classification sub-task: predicting the temporal orientation of search engine user queries.

ScreenShot

We tackled the task as a machine learning classification problem, by proposing th use of temporal-oriented attributes specifically designed to minimise the sparsity of the models.

The best submitted run achieved 66.33% of accuracy, by correctly predicting the temporal orientation of 199 test instances out of 300.

##Requirements

Python libraries:

Non-Python resources:

please update the feature_extractor.py file with the right paths.

##Contact

About

Our work for the NTCIR-11 Temporalia challenge, Temporal Query Intent Classification sub-task: predicting the temporal orientation of search engine user queries. We tackled the task as a machine learning classification problem, by proposing th use of temporal-oriented attributes specifically designed to minimise the sparsity of the models.

Resources

License

Releases

No releases published

Languages

You can’t perform that action at this time.