GloRE++

Data and code for ACL 2019 paper "Global Textual Relation Embedding for Relational Understanding"

Data

We will release the full relation graph soon!
The filtered relation graph and pre-trained models can be downloaded via Dropbox.

-GloREPlus
  |-small_graph --- The filtered relation graph used to train the textual relation embedding in this work.  
  |-train_data --- The filtered relation graph with the input format of the embedding model, for reference purpose.  
  |-models --- The pre-trained textual relation embedding models.  
  

For the filtered relation graph, we have the following format. The 3 columns are tab-separated:

textual_relation  KB_relation global_co-occurrence_statistics

Code

Requirements

python 2.7
tensorflow 1.6.0

Usage

-code  
  |-hyperparams.py ---  Hyperparamter settings and input/output file names.  
  |-train.py --- Train textual relation embedding model.  
  |-eval.py --- Generate textual relation embedding with pre-trained model.  
  |-model.py --- The Transformer embedding model.  
  |-baseline.py --- The RNN embedding model.  
  |-data_utils.py --- Utility functions.  
  
-data  
  |-kb_relation2id.txt --- Target KB relations.  
  |-vocab.txt --- Vocabulary file.  
  |-test.txt --- A sample textual relation input file.  
  
-model  
  to store the pre-trained models.  
  
-result  
  to store the result embeddings.  

Create two directories named model and result, to store pre-trained models and the result textual relation embeddings, respectively.

$ mkdir model  
$ mkdir result

Put the pre-trained embedding model under model/ directory. Change the model_dir variable in hyperparams.py to the name of the pre-trained model you want to use.
Prepare the input textual relations (parsed with universial dependency) in a single file, with one textual relation per line. The tokens (including both lexical words and dependency relations) are seperated by "##". Refer to a sample input file data/test.txt.

Put the the input textual relations file under data/ directory as test.txt. Specify your output file name as the output_file variable in hyperparams.py. Then run eval.py to produce embeddings of the input textual relations:

$ python eval.py  

The output file of the textual relation embeddings have the similar format as word2vec.