Fine-Grained Entity Recognizer (FIGER)

This distribution contains the source code for the experiments presented in the following research publication (PDF):

Xiao Ling and Daniel S. Weld (2012). 
"Fine-Grained Entity Recognition", 
in Proceedings OF THE TWENTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI), 2012. 

Download the model file

One can test the trained model on the evaluation data or new data as they wish.

Run ./downloadModel.sh to download the Model and save it at the root directory.

A better model has been trained and can be fetched by ./downloadModel.sh new. Change the config value accordingly.

Requirement

sbt >= 0.13.0

Replicate the experiments

To run the experiments in the AAAI-12 paper, you can proceed as follows:

$ ./run.sh "aaai/exp.conf" &> aaai/exp.log

Run FIGER on new data

To make predictions on new data, please see package edu.washington.cs.figer.FigerSystem for example code or run:

$ sbt "runMain edu.washington.cs.figer.FigerSystem <text_file>"

Alternatively, you can change the parameter values (e.g. the input file name) in config/figer.conf and get a more structured output by running:

$ ./run.sh "config/figer.conf"

Make a stand-alone jar

$ sbt assembly

A simple web interface

Run

$ sbt ~container:start

and go to localhost:8080/index.html for a simple web demo.

Training Data

The training data train.data.gz is gzipped and serialized in Protocol Buffer. Please see entity.proto in the code package for the definitions.

Download link

In config/figer.conf, make the following changes:

useModel=false
modelFile=<the output model file>

trainFile=<training file> # the training file has to follow the specs from `entity.proto`. See `train.data.gz` for example

Then run ./run.sh config/figer.conf to train a new model (It will need over 10G memory and about an hour to finish).

Mapping between Freebase MIDs and Wikipedia Titles

A mapping file from mids to titles is available here.