Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition

Overview

Usage

Environment setup and preparation

conda create --name pnr --file requirements.txt

• Place dataset under folder data
• Download pre-trained word embedding and set wordvec_path in the configuration file to the path of word embedding file.
• Download pre-trained language model (from huggingface) and set model_path and tokenizer_path in the configuration file to the path of the pre-trained language model.

Train

We train our model using RTX-3090 when training with ACE-04, ACE-05, KBP-17. And using RTX-A6000 when training with GENIA and Conll-03.

python main.py train --config cfg/ace05/train.conf

Evaluate

Set model_path and tokenzier_path to the checkpoint dir before running.

We provide our checkpoints on ACE04 and ACE05

python main.py eval --config cfg/ace05/eval.conf

Datasets

• ACE04: https://catalog.ldc.upenn.edu/LDC2005T09
• ACE05: https://catalog.ldc.upenn.edu/LDC2006T06
• KBP17: https://catalog.ldc.upenn.edu/LDC2017D55
• GENIA: http://www.geniaproject.org/genia-corpus
• CoNLL03: https://data.deepai.org/conll2003.zip

Format:

train/dev/test dataset is a .json file consists of a list of samples. Each sample is a dictionary with the following format:

{
    "tokens": ["Others", ",", "though", ",", "are", "novices", "."],
    "entities": [{"type": "PER", "start": 0, "end": 1}, {"type": "PER", "start": 5, "end": 6}], "relations": [], "org_id": "CNN_IP_20030328.1600.07",
    "ltokens": ["WOODRUFF", "We", "know", "that", "some", "of", "the", "American", "troops", "now", "fighting", "in", "Iraq", "are", "longtime", "veterans", "of", "warfare", ",", "probably", "not", "most", ",", "but", "some", ".", "Their", "military", "service", "goes", "back", "to", "the", "Vietnam", "era", "."],
    "rtokens": ["So", "what", "is", "it", "like", "for", "them", "to", "face", "combat", "far", "from", "home", "?", "For", "an", "idea", ",", "here", "is", "CNN", "'s", "Candy", "Crowley", "with", "some", "war", "stories", "."]
}

Entity types should be specified in a type.json file:

{
  "entities": {"GPE": {"short": "GPE", "verbose": "GPE"}, "ORG": {"short": "ORG", "verbose": "ORG"}, "PER": {"short": "PER", "verbose": "PER"}, "LOC": {"short": "LOC", "verbose": "LOC"}, "FAC": {"short": "FAC", "verbose": "FAC"}, "VEH": {"short": "VEH", "verbose": "VEH"}, "WEA": {"short": "WEA", "verbose": "WEA"}}
}

If you want to use part-of-speech tags as additional features, you should add a pos field for each sample like:

"pos": ["PROPN", "PROPN", "PROPN", ",", "PROPN", ",", "PROPN", "NOUN"]

And you should specify all pos tags in a pos.json file (pos.json and type.json should be placed in the same folder), the value of each tag represents the tag frequency:

{"PROPN": 15000, ",": 8169, "NOUN": 34923, "DET": 16595, "ADJ": 11268, "CCONJ": 4470, "VERB": 19302, "ADV": 6187, "AUX": 7046, "ADP": 17412, "NUM": 4154, ".": 8245, "X": 183, "(": 284, ")": 296, "PART": 5451, "SCONJ": 2225, "\"": 654, "PRON": 7613, "'": 1974, ":": 231, ";": 73, "-": 1366, "SYM": 437, "`": 1401, "INTJ": 194, "?": 203, "!": 26, "ReplayTV": 1, "/": 3, "_": 2, "s": 2, "]": 2, "}": 1, "{": 1}

Appendix

Please refer to Appendix

Citation

@inproceedings{ijcai2022p613,
  title     = {Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition},
  author    = {Wu, Shuhui and Shen, Yongliang and Tan, Zeqi and Lu, Weiming},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {4418--4424},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/613},
  url       = {https://doi.org/10.24963/ijcai.2022/613},
}