locate-and-label
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021.
Setup
Requirements
conda create --name acl python=3.8
conda activate acl
pip install -r requirements.txt
Datasets
The datasets used in our experiments:
- 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
Data format:
{
"tokens": ["2004-12-20T15:37:00", "Microscopic", "microcap", "Everlast", ",", "mainly", "a", "maker", "of", "boxing", "equipment", ",", "has", "soared", "over", "the", "last", "several", "days", "thanks", "to", "a", "licensing", "deal", "with", "Jacques", "Moret", "allowing", "Moret", "to", "buy", "out", "their", "women", "'s", "apparel", "license", "for", "$", "30", "million", ",", "on", "top", "of", "a", "$", "12.5", "million", "payment", "now", "."],
"pos": ["JJ", "JJ", "NN", "NNP", ",", "RB", "DT", "NN", "IN", "NN", "NN", ",", "VBZ", "VBN", "IN", "DT", "JJ", "JJ", "NNS", "NNS", "TO", "DT", "NN", "NN", "IN", "NNP", "NNP", "VBG", "NNP", "TO", "VB", "RP", "PRP$", "NNS", "POS", "NN", "NN", "IN", "$", "CD", "CD", ",", "IN", "NN", "IN", "DT", "$", "CD", "CD", "NN", "RB", "."],
"entities": [{"type": "ORG", "start": 1, "end": 4}, {"type": "ORG", "start": 5, "end": 11}, {"type": "ORG", "start": 25, "end": 27}, {"type": "ORG", "start": 28, "end": 29}, {"type": "ORG", "start": 32, "end": 33}, {"type": "PER", "start": 33, "end": 34}],
"ltokens": ["Everlast", "'s", "Rally", "Just", "Might", "Live", "up", "to", "the", "Name", "."],
"rtokens": ["In", "other", "words", ",", "a", "competitor", "has", "decided", "that", "one", "segment", "of", "the", "company", "'s", "business", "is", "potentially", "worth", "$", "42.5", "million", "."],
"org_id": "MARKETVIEW_20041220.1537"
}
The ltokens contains the tokens from the previous sentence. And The rtokens contains the tokens from the next sentence.
Due to the license of LDC, we cannot directly release our preprocessed datasets of ACE04, ACE05 and KBP17. We only release the preprocessed GENIA dataset and the corresponding word vectors and dictionary. Download them from here.
If you need other datasets, please contact me ([email protected]) by email. Note that you need to state your identity and prove that you have obtained the LDC license.
Pretrained Wordvecs
The word vectors used in our experiments:
- BioWord2Vec for GENIA: https://github.com/cambridgeltl/BioNLP-2016
- GloVe for other datasets: http://nlp.stanford.edu/data/glove.6B.zip
Download and extract the wordvecs from above links, save GloVe in ../glove and BioWord2Vec in ../biovec.
mkdir ../glove
mkdir ../biovec
mv glove.6B.100d.txt ../glove
mv PubMed-shuffle-win-30.txt ../biovec
Note: the BioWord2Vec downloaded from the above link is word2vec binary format, and needs to be converted to GloVe format. Refer to this.
Example
Train
python identifier.py train --config configs/example.conf
Note: You should edit this line in config_reader.py according to the actual number of GPUs.
Evaluation
You can download our checkpoints, or train your own model and then evaluate the model.
cd data/
# download checkpoints from https://drive.google.com/drive/folders/1NaoL42N-g1t9jiif427HZ6B8MjbyGTaZ?usp=sharing
unzip checkpoints.zip
cd ../
python identifier.py eval --config configs/eval.conf
If you use the checkpoints we provided, you will get the following results:
-
ACE05:
-- Entities (named entity recognition (NER)) ---
An entity is considered correct if the entity type and span is predicted correctlytype precision recall f1-score support WEA 84.62 88.00 86.27 50 ORG 83.23 78.78 80.94 523 PER 88.02 92.05 89.99 1724 FAC 80.65 73.53 76.92 136 GPE 85.13 87.65 86.37 405 VEH 86.36 75.25 80.42 101 LOC 66.04 66.04 66.04 53 micro 86.05 87.20 86.62 2992 macro 82.01 80.19 81.00 2992 -
GENIA:
--- Entities (named entity recognition (NER)) ---
An entity is considered correct if the entity type and span is predicted correctlytype precision recall f1-score support RNA 89.91 89.91 89.91 109 DNA 76.79 79.16 77.96 1262 cell_line 82.35 72.36 77.03 445 protein 81.11 85.18 83.09 3084 cell_type 72.90 75.91 74.37 606 micro 79.46 81.84 80.63 5506 macro 80.61 80.50 80.47 5506 -
ACE04:
--- Entities (named entity recognition (NER)) ---
An entity is considered correct if the entity type and span is predicted correctlytype precision recall f1-score support FAC 72.16 62.50 66.99 112 PER 91.62 91.26 91.44 1498 LOC 74.36 82.86 78.38 105 VEH 94.44 100.00 97.14 17 GPE 89.45 86.09 87.74 719 WEA 79.17 59.38 67.86 32 ORG 83.49 82.43 82.95 552 micro 88.24 86.79 87.51 3035 macro 83.53 80.64 81.78 3035 -
KBP17:
--- Entities (named entity recognition (NER)) ---
An entity is considered correct if the entity type and span is predicted correctlytype precision recall f1-score support LOC 66.75 64.41 65.56 399 FAC 72.62 64.06 68.08 679 PER 87.86 88.30 88.08 7083 ORG 80.06 72.29 75.98 2461 GPE 89.58 87.36 88.46 1978 micro 85.31 82.96 84.12 12600 macro 79.38 75.28 77.23 12600
Quick Start
The preprocessed GENIA dataset is available, so we use it as an example to demonstrate the training and evaluation of the model.
cd identifier
mkdir -p data/datasets
cd data/datasets
# download genia.zip (the preprocessed GENIA dataset, wordvec and vocabulary) from https://drive.google.com/file/d/13Lf_pQ1-QNI94EHlvtcFhUcQeQeUDq8l/view?usp=sharing.
unzip genia.zip
python identifier.py train --config configs/example.conf
output:
--- Entities (named entity recognition (NER)) ---
An entity is considered correct if the entity type and span is predicted correctly
type precision recall f1-score support
protein 81.19 85.08 83.09 3084
RNA 90.74 89.91 90.32 109
cell_line 82.35 72.36 77.03 445
DNA 76.83 79.08 77.94 1262
cell_type 72.90 75.91 74.37 606
micro 79.53 81.77 80.63 5506
macro 80.80 80.47 80.55 5506
Best F1 score: 80.63560463237275, achieved at Epoch: 34
2021-01-02 15:07:39,565 [MainThread ] [INFO ] Logged in: data/genia/main/genia_train/2021-01-02_05:32:27.317850
2021-01-02 15:07:39,565 [MainThread ] [INFO ] Saved in: data/genia/main/genia_train/2021-01-02_05:32:27.317850
vim configs/eval.conf
# change model_path to the path of the trained model.
# eg: model_path = data/genia/main/genia_train/2021-01-02_05:32:27.317850/final_model
python identifier.py eval --config configs/eval.conf
output:
--------------------------------------------------
Config:
data/checkpoint/genia_train/2021-01-02_05:32:27.317850/final_model
Namespace(bert_before_lstm=True, cache_path=None, char_lstm_drop=0.2, char_lstm_layers=1, char_size=50, config='configs/eval.conf', cpu=False, dataset_path='data/datasets/genia/genia_test_context.json', debug=False, device_id='0', eval_batch_size=4, example_count=None, freeze_transformer=False, label='2021-01-02_eval', log_path='data/genia/main/', lowercase=False, lstm_drop=0.2, lstm_layers=1, model_path='data/checkpoint/genia_train/2021-01-02_05:32:27.317850/final_model', model_type='identifier', neg_entity_count=5, nms=0.65, no_filter='sigmoid', no_overlapping=False, no_regressor=False, no_times_count=False, norm='sigmoid', pool_type='max', pos_size=25, prop_drop=0.5, reduce_dim=True, sampling_processes=4, seed=47, size_embedding=25, spn_filter=5, store_examples=True, store_predictions=True, tokenizer_path='data/checkpoint/genia_train/2021-01-02_05:32:27.317850/final_model', types_path='data/datasets/genia/genia_types.json', use_char_lstm=True, use_entity_ctx=True, use_glove=True, use_pos=True, use_size_embedding=False, weight_decay=0.01, window_size=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], wordvec_path='../biovec/PubMed-shuffle-win-30.txt')
Repeat 1 times
--------------------------------------------------
Iteration 0
--------------------------------------------------
Avaliable devices: [3]
Using Random Seed 47
2021-05-22 17:52:44,101 [MainThread ] [INFO ] Dataset: data/datasets/genia/genia_test_context.json
2021-05-22 17:52:44,101 [MainThread ] [INFO ] Model: identifier
Reused vocab!
Parse dataset 'test': 100%|███████████████████████████████████████| 1854/1854 [00:09<00:00, 202.86it/s]
2021-05-22 17:52:53,507 [MainThread ] [INFO ] Relation type count: 1
2021-05-22 17:52:53,507 [MainThread ] [INFO ] Entity type count: 6
2021-05-22 17:52:53,507 [MainThread ] [INFO ] Entities:
2021-05-22 17:52:53,507 [MainThread ] [INFO ] No Entity=0
2021-05-22 17:52:53,508 [MainThread ] [INFO ] DNA=1
2021-05-22 17:52:53,508 [MainThread ] [INFO ] RNA=2
2021-05-22 17:52:53,508 [MainThread ] [INFO ] cell_type=3
2021-05-22 17:52:53,508 [MainThread ] [INFO ] protein=4
2021-05-22 17:52:53,508 [MainThread ] [INFO ] cell_line=5
2021-05-22 17:52:53,508 [MainThread ] [INFO ] Relations:
2021-05-22 17:52:53,508 [MainThread ] [INFO ] No Relation=0
2021-05-22 17:52:53,508 [MainThread ] [INFO ] Dataset: test
2021-05-22 17:52:53,508 [MainThread ] [INFO ] Document count: 1854
2021-05-22 17:52:53,508 [MainThread ] [INFO ] Relation count: 0
2021-05-22 17:52:53,508 [MainThread ] [INFO ] Entity count: 5506
2021-05-22 17:52:53,508 [MainThread ] [INFO ] Context size: 242
2021-05-22 17:53:10,348 [MainThread ] [INFO ] Evaluate: test
Evaluate epoch 0: 100%|██████████████████████████████████████████| 464/464 [01:14<00:00, 6.26it/s]
Enmuberated Spans: 0
Evaluation
--- Entities (named entity recognition (NER)) ---
An entity is considered correct if the entity type and span is predicted correctly
type precision recall f1-score support
cell_line 82.60 71.46 76.63 445
DNA 77.67 78.29 77.98 1262
RNA 92.45 89.91 91.16 109
cell_type 73.79 75.25 74.51 606
protein 81.99 84.57 83.26 3084
micro 80.33 81.15 80.74 5506
macro 81.70 79.89 80.71 5506
2021-05-22 17:54:28,943 [MainThread ] [INFO ] Logged in: data/genia/main/genia_eval/2021-05-22_17:52:43.991876
Citation
If you have any questions related to the code or the paper, feel free to email [email protected].
@inproceedings{shen2021locateandlabel,
author = {Shen, Yongliang and Ma, Xinyin and Tan, Zeqi and Zhang, Shuai and Wang, Wen and Lu, Weiming},
title = {Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition},
url = {https://arxiv.org/abs/2105.06804},
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics",
year = {2021},
}
