安卓-figet-PROSAGA-码农传奇

Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds

Source code and data for StarSem‘18 paper Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds.

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Citation

The source code and data in this repository aims at facilitating the study of fine-grained entity typing. If you use the code/data, please cite it as follows:

@InProceedings{zhang-EtAl:2018:starSEM,
  author    = {Zhang, Sheng  and  Duh, Kevin  and  {Van Durme}, Benjamin},
  title     = {{Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds}},
  booktitle = {Proceedings of the 7th Joint Conference on Lexical and Computational Semantics (*SEM 2018)},
  month     = {June},
  year      = {2018}
}

Benchmark Performance

1. OntoNotes (Gillick et al., 2014)

Approach	Strict F1	Macro F1	Micro F1
Our Approach	55.52	73.33	67.61
w/o Adaptive thresholds	53.49	73.11	66.78
w/o Document-level contexts	53.17	72.14	66.51

2. Wiki (Ling and Weld, 2012)

Approach	Strict F1	Macro F1	Micro F1
Our Approach	60.23	78.67	75.52
w/o Adaptive thresholds	60.05	78.50	75.39

3. BBN (Weischedel and Brunstein, 2005)

Approach	Strict F1	Macro F1	Micro F1
Our Approach	60.87	77.75	76.94
w/o Adaptive thresholds	58.47	75.84	75.03
w/o Document-level contexts	58.12	75.65	75.11

Prerequisites

Python 2.7
PyTorch 0.2.0 (w/ CUDA support)
Numpy
tqdm

Running

Once getting the prerequisites, you can run the whole process very easily. Take the OntoNotes corpus for example,

Step 1: Download the data

./scripts/ontonotes.sh get_data

Step 2: Preprocess the data

./scripts/ontonotes.sh preprocess

Step 3: Train the model

./scripts/ontonotes.sh train

Step 4: Tune the threshold

./scripts/ontonotes.sh adaptive-thres

Step 5: Do inference

./scripts/ontonotes.sh inference

Acknowledgements

The datasets (Wiki and OntoNotes) are copies from Sonse Shimaoka’s repository.

License

MIT