In this task, noun-compounds are annotated to a pre-defined set of relations, and the model has to predict the correct
relation between the constituents of an unobserved compound. For example, olive oil may belong to the SOURCE relation
while morning meeting belongs to the TIME relation.
We train models whose input is the noun compound representation.
usage: classifier.py [-h] in_model_path dataset_prefix out_model_dir
positional arguments:
in_model_path word embeddings or composition model path
dataset_prefix path to the train/test/val/rel data
out_model_dir where to store the result
optional arguments:
-h, --help show this help message and exit
We use the Tratz (2011) dataset in several variants (fine or coarse-grained relation inventory, random or lexical data split), which can be found under data.
To run all:
bash train_all_classifiers.sh