PlanarizedSentence

Source code for : Planarized Sentence Representation for Nested Named Entity Recognition

We will upload the code one after another.

Installation

Environment

python=3.7
pytorch=1.8.0
transformers=4.24.0
gensim=4.2.0
scikit-learn=1.0.2
prettytable=3.5.0
cudatoolkit=11.1
numpy=1.21.5

Dataset

• ACE 2004
• ACE 2005
• GENIA
• KBP 2017
• NNE
• CoNLL 2003
• Resume

Data format

[
  {
  "sentence": 
  ["There", "is", "a", "single", "methionine", "codon-initiated", "open", "reading", "frame", "of", "1,458", "nt", "in", "frame", "with", "a", "homeobox", "and", "a", "CAX", "repeat", ",", "and", "the", "open", "reading", "frame", "is", "predicted", "to", "encode", "a", "protein", "of", "51,659", "daltons."], 
"ner": [
{"index": [16], "type": "DNA"}, 
{"index": [4, 5, 6, 7, 8], "type": "DNA"}, 
{"index": [24, 25, 26], "type": "DNA"}, 
{"index": [19, 20], "type": "DNA"}
        ]
    }
]

Due to the license of LDC, we can't directly release our preprocessed datasets of ACE2004, ACE2005, KBP17, and NNE.

Running the Code

$ python main.py

References

• Li J, Fei H, Liu J, et al. Unified named entity recognition as word-word relation classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(10): 10965-10973.
• Shen Y, Ma X, Tan Z, et al. Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021: 2782-2794.
• Chen, Y., Wu, L., Zheng, Q. et al. A Boundary Regression Model for Nested Named Entity Recognition. Cogn Comput (2022). https://doi.org/10.1007/s12559-022-10058-8