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.

Also includes implementation of PCNN, PCNN+ATT, CNN, CNN+ATT, and BGWA models.

• Compatible with TensorFlow 1.x and Python 3.x.
• Dependencies can be installed using requirements.txt.

• We use Riedel NYT and Google IISc Distant Supervision (GIDS) dataset​ for evaluation.

• Datasets in json list format with side information can be downloaded from here: RiedelNYT and GIDS.

• The processed version of the datasets can be downloaded from RiedelNYT and GIDS. The structure of the processed input data is as follows.

  {
      "voc2id":   {"w1": 0, "w2": 1, ...},
      "type2id":  {"type1": 0, "type2": 1 ...},
      "rel2id":   {"NA": 0, "/location/neighborhood/neighborhood_of": 1, ...}
      "max_pos": 123,
      "train": [
          {
              "X":        [[s1_w1, s1_w2, ...], [s2_w1, s2_w2, ...], ...],
              "Y":        [bag_label],
              "Pos1":     [[s1_p1_1, sent1_p1_2, ...], [s2_p1_1, s2_p1_2, ...], ...],
              "Pos2":     [[s1_p2_1, sent1_p2_2, ...], [s2_p2_1, s2_p2_2, ...], ...],
              "SubPos":   [s1_sub, s2_sub, ...],
              "ObjPos":   [s1_obj, s2_obj, ...],
              "SubType":  [s1_subType, s2_subType, ...],
              "ObjType":  [s1_objType, s2_objType, ...],
              "ProbY":    [[s1_rel_alias1, s1_rel_alias2, ...], [s2_rel_alias1, ... ], ...]
              "DepEdges": [[s1_dep_edges], [s2_dep_edges] ...]
          },
          {}, ...
      ],
      "test":  { same as "train"},
      "valid": { same as "train"},
  }

  • voc2id is the mapping of word to its id
  • type2id is the maping of entity type to its id.
  • rel2id is the mapping of relation to its id.
  • max_pos is the maximum position to consider for positional embeddings.
  • Each entry of train, test and valid is a bag of sentences, where
    • X denotes the sentences in bag as the list of list of word indices.
    • Y is the relation expressed by the sentences in the bag.
    • Pos1 and Pos2 are position of each word in sentences wrt to target entity 1 and entity 2.
    • SubPos and ObjPos contains the position of the target entity 1 and entity 2 in each sentence.
    • SubType and ObjType contains the target entity 1 and entity 2 type information obtained from KG.
    • ProbY is the relation alias side information (refer paper) for the bag.
    • DepEdges is the edgelist of dependency parse for each sentence (required for GCN).

• reside.py contains TensorFlow (1.x) based implementation of RESIDE (proposed method).
• Download the pretrained model's parameters from RiedelNYT and GIDS (put downloaded folders in checkpoint directory).
• Execute evaluate.sh for comparing pretrained RESIDE model against baselines (plots Precision-Recall curve).

• Entity Type information for both the datasets is provided in side_info/type_info.zip.
  • Entity type information can be used directly in the model.
• Relation Alias Information for both the datasets is provided in side_info/relation_alias.zip.
  • The preprocessing code for using relation alias information: rel_alias_side_info.py.
  • Following figure summarizes the method:

• Execute setup.sh for downloading GloVe embeddings.
• For training RESIDE run:

  python reside.py -data data/riedel_processed.pkl -name new_run

• The above model needs to be further trained with SGD optimizer for few epochs to match the performance reported in the paper. For that execute

  python reside.py -name new_run -restore -opt sgd -lr 0.001 -l2 0.0 -epoch 4

• Finally, run python plot_pr.py -name new_run to get the plot.

• The repository also includes code for PCNN, PCNN+ATT, CNN, CNN+ATT, BGWA models.

• For training PCNN+ATT:

  python pcnnatt.py -data data/riedel_processed.pkl -name new_run -attn # remove -attn for PCNN

• Similarly for training CNN+ATT:

  python cnnatt.py -data data/riedel_processed.pkl -name new_run # remove -attn for CNN

• For training BGWA:

  python bgwa.py -data data/riedel_processed.pkl -name new_run

• preproc directory contains code for getting a new dataset in the required format (riedel_processed.pkl) for reside.py.
• Get the data in the same format as followed in riedel_raw or gids_raw for Riedel NYT dataset.
• Finally, run the script preprocess.sh. make_bags.py is used for generating bags from sentence. generate_pickle.py is for converting the data in the required pickle format.

• The code for running pretrained model on a sample is included in online directory.

• A flask based server is also provided. Use python online/server.py to start the server.

  • riedel_test_bags.json and other required files can be downloaded from the provided links.

  

Please cite the following paper if you use this code in your work.

@inproceedings{reside2018,
  author = 	"Vashishth, Shikhar and 
  		Joshi, Rishabh and
		Prayaga, Sai Suman and
		Bhattacharyya, Chiranjib and
		Talukdar, Partha",
  title = 	"{RESIDE}: Improving Distantly-Supervised Neural Relation Extraction using Side Information",
  booktitle = 	"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  month = 	oct # "-" # nov,
  address = 	"Brussels, Belgium",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"1257--1266",
  url = 	"http://aclweb.org/anthology/D18-1157"
}

For any clarification, comments, or suggestions please create an issue or contact Shikhar.