This page contains code of the neural tagging library targer, which is a part of the TARGER project. The code and data are related to the following paper:
Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, and Alexander Panchenko (2019): TARGER: Neural Argument Mining at Your Fingertips. In Proceedings of the 57th Annual Meeting of the Association of Computational Linguistics (ACL'2019). Florence, Italy.
If you use the demo or would like to refer to it, please cite the paper mentioned above. You can also use the following BibTex information for citation:
@inproceedings{chernodub2019targer,
title={TARGER: Neural Argument Mining at Your Fingertips},
author={Chernodub, Artem and Oliynyk, Oleksiy and Heidenreich, Philipp and Bondarenko, Alexander and Hagen, Matthias and Biemann, Chris and Panchenko, Alexander},
booktitle={Proceedings of the 57th Annual Meeting of the Association of Computational Linguistics (ACL'2019)},
year={2019},
address={Florence, Italy}
}
Web application which is also a part of TARGER project lives in a separate repository https://github.com/uhh-lt/targer. There you will find instructions on how to run it locally.
URL for accessing TARGER web-application and web-service online: http://ltdemos.informatik.uni-hamburg.de/targer/.
targer is a PyTorch implementation of "mainstream" BiLSTM-CNN-CRF neural tagging method based on works of Lample, et. al., 2016 and Ma et. al., 2016.
- Python 3.5.2 or higher
- NumPy 1.15.1
- SciPy 1.1.0
- PyTorch >= 0.4.1
- Native PyTorch implementation.
- Vectorized code for training on batches.
- Easy addition of new classes for custom data file formats and evaluation metrics.
|__ articles/ --> collection of papers related to the tagging, argument mining, etc.
|__ data/
|__ NER/ --> Datasets for Named Entity Recognition
|__ CoNNL_2003_shared_task/ --> data for NER CoNLL-2003 shared task (English) in BOI-2
CoNNL format, from E.F. Tjong Kim Sang and F. De Meulder,
Introduction to the CoNLL-2003 Shared Task:
Language-Independent Named Entity Recognition, 2003.
|__ AM/ --> Datasets for Argument Mining
|__ persuasive_essays/ --> data for persuasive essays in BOI-2-like CoNNL format, from:
Steffen Eger, Johannes Daxenberger, Iryna Gurevych. Neural
End-to-End Learning for Computational Argumentation Mining, 2017
|__ docs/ --> documentation
|__ embeddings
|__ get_glove_embeddings.sh --> script for downloading GloVe6B 100-dimensional word embeddings
|__ get_fasttext_embeddings.sh --> script for downloading Fasttext word embeddings
|__ pretrained/
|__ tagger_NER.hdf5 --> tagger for NER, BiLSTM+CNN+CRF trained on NER-2003 shared task, English
src/
|__utils/
|__generate_tree_description.py --> import os
|__generate_ft_emb.py --> generate predefined FastText embeddings for dataset
|__models/
|__tagger_base.py --> abstract base class for all types of taggers
|__tagger_birnn.py --> Vanilla recurrent network model for sequences tagging.
|__tagger_birnn_crf.py --> BiLSTM/BiGRU + CRF tagger model
|__tagger_birnn_cnn.py --> BiLSTM/BiGRU + char-level CNN tagger model
|__tagger_birnn_cnn_crf.py --> BiLSTM/BiGRU + char-level CNN + CRF tagger model
|__data_io/
|__data_io_connl_ner_2003.py --> input/output data wrapper for CoNNL file format used in NER-2003 Shared Task dataset
|__data_io_connl_pe.py --> input/output data wrapper for CoNNL file format used in Persuassive Essays dataset
|__data_io_connl_wd.py --> input/output data wrapper for CoNNL file format used in Web Discourse dataset
|__factories/
|__factory_datasets_bank.py --> creates various datasets banks
|__factory_data_io.py --> creates various data readers/writers
|__factory_evaluator.py --> creates various evaluators
|__factory_optimizer.py --> creates various optimizers
|__factory_tagger.py --> creates various tagger models
|__layers/
|__layer_base.py --> abstract base class for all type of layers
|__layer_word_embeddings.py --> class implements word embeddings
|__layer_char_cnn.py --> class implements character-level convolutional 1D layer
|__layer_char_embeddings.py --> class implements character-level embeddings
|__layer_birnn_base.py --> abstract base class for all bidirectional recurrent layers
|__layer_bivanilla.py --> class implements standard bidirectional Vanilla recurrent layer
|__layer_bilstm.py --> class implements standard bidirectional LSTM recurrent layer
|__layer_bigru.py --> class implements standard bidirectional GRU recurrent layer
|__layer_crf.py --> class implements Conditional Random Fields (CRF)
|__evaluators/
|__evaluator_base.py --> abstract base class for all evaluators
|__evaluator_acc_token_level.py --> token-level accuracy evaluator for each class of BOI-like tags
|__evaluator_f1_macro_token_level.py --> macro-F1 scores evaluator for each class of BOI-like tags
|__evaluator_f1_micro_spans_connl.py --> f1-micro averaging evaluator for tag components, spans detection + classification, uses standard CoNNL perl script
|__evaluator_f1_micro_spans_alpha_match_base.py --> abstract base class for f1-micro averaging evaluation for tag components, spans detection + classification
|__evaluator_f1_micro_spans_alpha_match_05.py --> f1-micro averaging evaluation for tag components, spans detection + classification, alpha = 0.5
|__evaluator_f1_micro_spans_alpha_match_10.py --> f1-micro averaging evaluation for tag components, spans detection + classification, alpha = 1.0 (strict)
|__seq_indexers/
|__seq_indexer_base.py --> base abstract class for sequence indexers
|__seq_indexer_base_embeddings.py --> abstract sequence indexer class that implements work with embeddings
|__seq_indexer_word.py --> converts list of lists of words as strings to list of lists of integer indices and back
|__seq_indexer_char.py --> converts list of lists of characters to list of lists of integer indices and back
|__seq_indexer_tag.py --> converts list of lists of string tags to list of lists of integer indices and back
|__classes/
|__datasets_bank.py --> provides storing the train/dev/test data subsets and sampling batches from the train dataset
|__report.py --> stores evaluation results during the training process as text files
|__utils.py --> several auxiliary functions
|__ main.py --> main script for training/evaluation/saving tagger models
|__ run_tagger.py --> run the trained tagger model from the checkpoint file
|__ conlleval --> "official" Perl script from NER 2003 shared task for evaluating the f1 scores,
author: Erik Tjong Kim Sang, version: 2004-01-26
|__ requirements.txt --> file for managing packages requirements
Results of training the models (see the settings here):
| tagger model | dataset | micro-f1 on test |
|---|---|---|
| BiLSTM + CNN + CRF Lample et. al., 2016 | NER-2003 shared task (English) | 90.94 |
| BiLSTM + CNN + CRF Ma et al., 2016 | NER-2003 shared task (English) | 91.21 |
| BiLSTM + CNN + CRF (our) | NER-2003 shared task (English) | 90.59 |
| STag_BLCC, Eger et. al., 2017 | AM Persuasive Essays, Paragraph Level | 64.74 +/- 1.97 |
| BiLSTM + CNN + CRF (our) | AM Persuasive Essays, Paragraph Level | 64.54 |
In order to ensure the consistency of the experiments, for evaluation purposes we use "official" Perl script from NER 2003 shared task, author: Erik Tjong Kim Sang, version: 2004-01-26, example of it's output:
Standard CoNNL perl script (author: Erik Tjong Kim Sang <[email protected]>, version: 2004-01-26):
processed 46435 tokens with 5648 phrases; found: 5622 phrases; correct: 5105.
accuracy: 97.92%; precision: 90.80%; recall: 90.39%; FB1: 90.59
LOC: precision: 93.06%; recall: 91.67%; FB1: 92.36 1643
MISC: precision: 78.75%; recall: 80.77%; FB1: 79.75 720
ORG: precision: 88.57%; recall: 88.62%; FB1: 88.59 1662
PER: precision: 96.24%; recall: 95.05%; FB1: 95.64 1597
To train/evaluate/save trained tagger model, please run the main.py script.
usage: main.py [-h] [--train TRAIN] [--dev DEV] [--test TEST]
[-d {connl-ner-2003,connl-pe,connl-wd}] [--gpu GPU]
[--model {BiRNN,BiRNNCNN,BiRNNCRF,BiRNNCNNCRF}] [--load LOAD]
[--save SAVE] [--word-seq-indexer WORD_SEQ_INDEXER]
[--epoch-num EPOCH_NUM] [--min-epoch-num MIN_EPOCH_NUM]
[--patience PATIENCE]
[--evaluator {f1-connl,f1-alpha-match-10,f1-alpha-match-05,f1-macro,token-acc}]
[--save-best [{yes,True,no default),False}]]
[--dropout-ratio DROPOUT_RATIO] [--batch-size BATCH_SIZE]
[--opt {sgd,adam}] [--lr LR] [--lr-decay LR_DECAY]
[--momentum MOMENTUM] [--clip-grad CLIP_GRAD]
[--rnn-type {Vanilla,LSTM,GRU}]
[--rnn-hidden-dim RNN_HIDDEN_DIM] [--emb-fn EMB_FN]
[--emb-dim EMB_DIM] [--emb-delimiter EMB_DELIMITER]
[--emb-load-all [{yes,True,no (default),False}]]
[--freeze-word-embeddings [{yes,True,no (default),False}]]
[--check-for-lowercase [{yes (default),True,no,False}]]
[--char-embeddings-dim CHAR_EMBEDDINGS_DIM]
[--char-cnn_filter-num CHAR_CNN_FILTER_NUM]
[--char-window-size CHAR_WINDOW_SIZE]
[--freeze-char-embeddings [{yes,True,no (default),False}]]
[--word-len WORD_LEN]
[--dataset-sort [{yes,True,no (default),False}]]
[--seed-num SEED_NUM] [--report-fn REPORT_FN]
[--cross-folds-num CROSS_FOLDS_NUM]
[--cross-fold-id CROSS_FOLD_ID]
[--verbose [{yes (default,True,no,False}]]
Learning tagger using neural networks
optional arguments:
-h, --help show this help message and exit
--train TRAIN Train data in format defined by --data-io param.
--dev DEV Development data in format defined by --data-io param.
--test TEST Test data in format defined by --data-io param.
-d {connl-ner-2003,connl-pe,connl-wd}, --data-io {connl-ner-2003,connl-pe,connl-wd}
Data read/write file format.
--gpu GPU GPU device number, -1 means CPU.
--model {BiRNN,BiRNNCNN,BiRNNCRF,BiRNNCNNCRF}
Tagger model.
--load LOAD, -l LOAD Path to load from the trained model.
--save SAVE, -s SAVE Path to save the trained model.
--word-seq-indexer WORD_SEQ_INDEXER, -w WORD_SEQ_INDEXER
Load word_seq_indexer object from hdf5 file.
--epoch-num EPOCH_NUM, -e EPOCH_NUM
Number of epochs.
--min-epoch-num MIN_EPOCH_NUM, -n MIN_EPOCH_NUM
Minimum number of epochs.
--patience PATIENCE, -p PATIENCE
Patience for early stopping.
--evaluator {f1-connl,f1-alpha-match-10,f1-alpha-match-05,f1-macro,token-acc}, -v {f1-connl,f1-alpha-match-10,f1-alpha-match-05,f1-macro,token-acc}
Evaluation method.
--save-best [{yes,True,no (default),False}]
Save best on dev model as a final model.
--dropout-ratio DROPOUT_RATIO, -r DROPOUT_RATIO
Dropout ratio.
--batch-size BATCH_SIZE, -b BATCH_SIZE
Batch size, samples.
--opt {sgd,adam}, -o {sgd,adam}
Optimization method.
--lr LR Learning rate.
--lr-decay LR_DECAY Learning decay rate.
--momentum MOMENTUM, -m MOMENTUM
Learning momentum rate.
--clip-grad CLIP_GRAD
Clipping gradients maximum L2 norm.
--rnn-type {Vanilla,LSTM,GRU}
RNN cell units type.
--rnn-hidden-dim RNN_HIDDEN_DIM
Number hidden units in the recurrent layer.
--emb-fn EMB_FN Path to word embeddings file.
--emb-dim EMB_DIM Dimension of word embeddings file.
--emb-delimiter EMB_DELIMITER
Delimiter for word embeddings file.
--emb-load-all [{yes,True,no (default),False}]
Load all embeddings to model.
--freeze-word-embeddings [{yes,True,no (default),False}]
False to continue training the word embeddings.
--check-for-lowercase [{yes (default),True,no,False}]
Read characters caseless.
--char-embeddings-dim CHAR_EMBEDDINGS_DIM
Char embeddings dim, only for char CNNs.
--char-cnn_filter-num CHAR_CNN_FILTER_NUM
Number of filters in Char CNN.
--char-window-size CHAR_WINDOW_SIZE
Convolution1D size.
--freeze-char-embeddings [{yes,True,no (default),False}]
False to continue training the char embeddings.
--word-len WORD_LEN Max length of words in characters for char CNNs.
--dataset-sort [{yes,True,no (default),False}]
Sort sequences by length for training.
--seed-num SEED_NUM Random seed number, note that 42 is the answer.
--report-fn REPORT_FN
Report filename.
--cross-folds-num CROSS_FOLDS_NUM
Number of folds for cross-validation (optional, for
some datasets).
--cross-fold-id CROSS_FOLD_ID
Current cross-fold, 1<=cross-fold-id<=cross-folds-num
(optional, for some datasets).
--verbose [{yes (default),True,no,False}]
Show additional information.
Start run_tagger.py.
usage: run_tagger.py [-h] [--output OUTPUT]
[--data-io {connl-ner-2003,connl-pe,connl-wd}]
[--evaluator {f1-connl,f1-alpha-match-10,f1-alpha-match-05,f1-macro,token-acc}]
[--gpu GPU]
load input
Run trained model
positional arguments:
load Path to load from the trained model.
input Input CoNNL filename.
optional arguments:
-h, --help show this help message and exit
--output OUTPUT, -o OUTPUT
Output JSON filename.
--data-io {connl-ner-2003,connl-pe,connl-wd}, -d {connl-ner-2003,connl-pe,connl-wd}
Data read file format.
--evaluator {f1-connl,f1-alpha-match-10,f1-alpha-match-05,f1-macro,token-acc}, -v {f1-connl,f1-alpha-match-10,f1-alpha-match-05,f1-macro,token-acc}
Evaluation method.
--gpu GPU, -g GPU GPU device number, 0 by default, -1 means CPU.
Evaluation
batch_size=10
char_cnn_filter_num=30
char_embeddings_dim=25
char_window_size=3
check_for_lowercase=True
clip_grad=5
cross_fold_id=-1
cross_folds_num=-1
data_io='connl-ner-2003'
dataset_sort=False
dev='data/NER/CoNNL_2003_shared_task/dev.txt'
dropout_ratio=0.5
emb_delimiter=' '
emb_dim=100
emb_fn='embeddings/glove.6B.100d.txt'
emb_load_all=False
epoch_num=100
evaluator='f1-connl'
freeze_char_embeddings=False
freeze_word_embeddings=False
gpu=0
load=None
lr=0.01
lr_decay=0.05
min_epoch_num=50
model='BiRNNCNNCRF'
momentum=0.9
opt='sgd'
patience=15
report_fn='2019_01_21_18-59_27_report.txt'
rnn_hidden_dim=100
rnn_type='LSTM'
save='2019_01_21_18-59_27_tagger.hdf5'
save_best=True
seed_num=42
test='data/NER/CoNNL_2003_shared_task/test.txt'
train='data/NER/CoNNL_2003_shared_task/train.txt'
verbose=True
word_len=20
word_seq_indexer=None
epoch | train loss | f1-connl-train | f1-connl-dev | f1-connl-test
--------------------------------------------------------------------------------------
0 | 0.00 | 2.80 | 2.13 | 5.32
1 | 289.14 | 78.75 | 78.50 | 77.45
2 | 152.54 | 85.58 | 86.30 | 82.26
3 | 113.81 | 89.69 | 89.21 | 86.46
4 | 97.00 | 90.32 | 89.08 | 85.63
5 | 79.96 | 91.38 | 90.07 | 86.23
6 | 75.38 | 90.08 | 88.59 | 84.31
7 | 73.16 | 92.88 | 90.97 | 87.07
8 | 65.99 | 93.12 | 91.11 | 88.10
9 | 63.29 | 92.85 | 91.31 | 87.24
10 | 59.74 | 94.26 | 91.80 | 88.62
11 | 57.86 | 92.43 | 90.07 | 85.36
12 | 59.29 | 94.45 | 91.56 | 88.43
13 | 49.88 | 94.78 | 91.59 | 88.79
14 | 43.62 | 95.25 | 91.94 | 88.91
15 | 49.21 | 95.52 | 92.51 | 88.80
16 | 48.19 | 95.64 | 92.58 | 89.09
17 | 41.08 | 95.74 | 92.37 | 88.86
18 | 42.94 | 95.98 | 92.35 | 89.23
19 | 40.35 | 96.25 | 92.59 | 88.93
20 | 37.66 | 95.83 | 92.49 | 88.31
21 | 34.63 | 96.51 | 92.78 | 89.22
22 | 34.85 | 96.28 | 92.68 | 89.17
23 | 33.07 | 96.61 | 92.99 | 89.34
24 | 36.03 | 96.31 | 92.78 | 89.11
25 | 32.11 | 96.70 | 92.87 | 89.20
26 | 29.25 | 96.95 | 93.72 | 89.87
27 | 33.87 | 97.16 | 93.38 | 89.59
28 | 34.77 | 97.30 | 93.82 | 89.95
29 | 28.70 | 96.97 | 93.20 | 89.54
30 | 30.73 | 96.92 | 93.10 | 89.13
31 | 29.28 | 97.34 | 93.51 | 89.93
32 | 28.76 | 97.10 | 92.93 | 88.81
33 | 28.58 | 97.56 | 93.55 | 90.09
34 | 24.82 | 97.18 | 93.58 | 89.82
35 | 28.73 | 97.81 | 94.01 | 90.21
36 | 24.40 | 97.72 | 93.59 | 89.82
37 | 24.64 | 97.85 | 93.79 | 89.87
38 | 29.51 | 97.96 | 93.82 | 90.08
39 | 27.63 | 98.05 | 94.30 | 90.05
40 | 25.07 | 98.02 | 94.15 | 90.08
41 | 22.76 | 98.16 | 94.10 | 90.20
42 | 22.73 | 97.96 | 93.86 | 89.92
43 | 22.78 | 98.37 | 94.41 | 90.59
44 | 22.38 | 98.24 | 94.05 | 90.25
45 | 19.20 | 98.33 | 94.10 | 89.90
46 | 21.02 | 98.30 | 93.97 | 90.13
47 | 22.39 | 98.25 | 94.17 | 90.53
48 | 19.91 | 98.30 | 94.15 | 90.09
49 | 20.66 | 98.17 | 93.61 | 90.15
50 | 18.91 | 98.34 | 93.84 | 89.81
51 | 19.94 | 98.25 | 93.82 | 90.00
52 | 18.10 | 98.44 | 94.10 | 90.09
53 | 17.20 | 98.52 | 94.30 | 90.29
54 | 20.11 | 98.38 | 93.92 | 90.36
55 | 17.46 | 98.51 | 94.34 | 90.29
56 | 19.65 | 98.38 | 94.05 | 90.07
57 | 18.48 | 98.47 | 93.98 | 90.11
58 | 19.20 | 98.38 | 94.01 | 90.10
59 | 16.50 | 98.68 | 94.06 | 90.09
--------------------------------------------------------------------------------------
Final eval on test, "save best", best epoch on dev 43, f1-connl, test = 90.59)
--------------------------------------------------------------------------------------
Standard CoNNL perl script (author: Erik Tjong Kim Sang <[email protected]>, version: 2004-01-26):
processed 46435 tokens with 5648 phrases; found: 5622 phrases; correct: 5105.
accuracy: 97.92%; precision: 90.80%; recall: 90.39%; FB1: 90.59
LOC: precision: 93.06%; recall: 91.67%; FB1: 92.36 1643
MISC: precision: 78.75%; recall: 80.77%; FB1: 79.75 720
ORG: precision: 88.57%; recall: 88.62%; FB1: 88.59 1662
PER: precision: 96.24%; recall: 95.05%; FB1: 95.64 1597
Input arguments:
python3 main.py
Training on NER-2003 Shared dataset:
python3 main.py
Training on Peruassive Essays dataset:
python3 main.py --train data/AM/persuasive_essays/Paragraph_Level/train.dat.abs --dev data/AM/persuasive_essays/Paragraph_Level/dev.dat.abs --test data/AM/persuasive_essays/Paragraph_Level/test.dat.abs --data-io connl-pe --evaluator f1-alpha-match-10 --opt adam --lr 0.001 --save-best yes --patience 20 --rnn-hidden-dim 200
Training on Web Discourse dataset (cross-validation):
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 1;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 2;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 3;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 4;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 5;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 6;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 7;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 8;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 9;
python3 main.py --train data/AM/web_discourse --evaluator f1-macro --data-io connl-wd --opt adam --lr 0.001 --save-best yes -w w_wd.hdf5 --rnn-hidden-dim 250 --cross-folds-num 10 --cross-fold-id 10;
- NeuroNER (Tensorflow) https://github.com/Franck-Dernoncourt/NeuroNER
- LM-LSTM-CRF (Pytorch) https://github.com/LiyuanLucasLiu/LM-LSTM-CRF
- LD-Net (Pytorch) https://github.com/LiyuanLucasLiu/LD-Net
- LSTM-CRF in PyTorch (Pytorch) https://github.com/threelittlemonkeys/lstm-crf-pytorch
- UKPLab/emnlp2017-bilstm-cnn-crf (Tensorflow & Keras) https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf
- UKPLab/elmo-bilstm-cnn-crf (Tensorflow & Keras) https://github.com/UKPLab/elmo-bilstm-cnn-crf