RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
Source code for EMNLP 2018 paper: RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information.
Overview of RESIDE (proposed method): RESIDE first encodes each sentence in the bag by concatenating embeddings (denoted by ⊕) from Bi-GRU and Syntactic GCN for each token, followed by word attention. Then, sentence embedding is concatenated with relation alias information, which comes from the Side Information Acquisition Section, before computing attention over sentences. Finally, bag representation with entity type information is fed to a softmax classifier. Please refer to paper for more details.
- Compatible with TensorFlow 1.x and Python 3.x.
- Dependencies can be installed using
- We use Riedel NYT and Google IISc Distant Supervision (GIDS) dataset for evaluation.
- The processed version of the datasets can be downloaded from here.
After installing python dependencies from
sh setup.shfor downloading GloVe embeddings.
reside.pycontains TensorFlow (1.x) based implementation of the RESIDE (proposed method).
For running pre-trained version of RESIDE:
python reside.py -data data/riedel_processed.pkl -name pretrained_model -restore
For training model from scratch:
python reside.py -data data/riedel_processed.pkl -name test_run