https://github.com/shangjingbo1226/autoner
Learning Named Entity Tagger from Domain-Specific Dictionary
https://github.com/shangjingbo1226/autoner
data-driven dictionary distant-supervision domain-specific named-entity-recognition ner sequence-labeling
Last synced: 8 months ago
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Learning Named Entity Tagger from Domain-Specific Dictionary
- Host: GitHub
- URL: https://github.com/shangjingbo1226/autoner
- Owner: shangjingbo1226
- License: apache-2.0
- Created: 2018-07-06T00:03:11.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-10-05T08:29:47.000Z (about 6 years ago)
- Last Synced: 2025-01-03T13:44:55.225Z (10 months ago)
- Topics: data-driven, dictionary, distant-supervision, domain-specific, named-entity-recognition, ner, sequence-labeling
- Language: ChucK
- Homepage: https://shangjingbo1226.github.io/AutoNER/
- Size: 3.55 MB
- Stars: 482
- Watchers: 15
- Forks: 91
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AutoNER
**Check Our New NER Toolkit🚀🚀🚀**
- **Inference**:
- **[LightNER](https://github.com/LiyuanLucasLiu/LightNER)**: inference w. models pre-trained / trained w. *any* following tools, *efficiently*.
- **Training**:
- **[LD-Net](https://github.com/LiyuanLucasLiu/LD-Net)**: train NER models w. efficient contextualized representations.
- **[VanillaNER](https://github.com/LiyuanLucasLiu/Vanilla_NER)**: train vanilla NER models w. pre-trained embedding.
- **Distant Training**:
- **[AutoNER](https://shangjingbo1226.github.io/AutoNER/)**: train NER models w.o. line-by-line annotations and get competitive performance.
--------------------------------
[](https://opensource.org/licenses/Apache-2.0)
[](http://autoner.readthedocs.io/en/latest/?badge=latest)
**No line-by-line annotations**, AutoNER trains named entity taggers with distant supervision.
Details about AutoNER can be accessed at: [https://arxiv.org/abs/1809.03599](https://arxiv.org/abs/1809.03599)
- [Model notes](#model-notes)
- [Benchmarks](#benchmarks)
- [Training](#training)
- [Required Inputs](#required-inputs)
- [Dependencies](#dependencies)
- [Command](#command)
- [Citation](#citation)
## Model Notes
## Benchmarks
| Method | Precision | Recall | F1 |
| ------------- |-------------| -----| -----|
| Supervised Benchmark | 88.84 | 85.16 | **86.96** |
| Dictionary Match | 93.93 | 58.35 | 71.98 |
| Fuzzy-LSTM-CRF | 88.27 | 76.75 | 82.11 |
| AutoNER | 88.96 | 81.00 | **84.80** |
## Training
### Required Inputs
- **Tokenized Raw Texts**
- Example: ```data/BC5CDR/raw_text.txt```
- One token per line.
- An empty line means the end of a sentence.
- **Two Dictionaries**
- **Core Dictionary w/ Type Info**
- Example: ```data/BC5CDR/dict_core.txt```
- Two columns (i.e., Type, Tokenized Surface) per line.
- Tab separated.
- How to obtain?
- From domain-specific dictionaries.
- **Full Dictionary w/o Type Info**
- Example: ```data/BC5CDR/dict_full.txt```
- One tokenized high-quality phrases per line.
- How to obtain?
- From domain-specific dictionaries.
- Applying the high-quality phrase mining tool on domain-specific corpus.
- [AutoPhrase](https://github.com/shangjingbo1226/AutoPhrase)
- **Pre-trained word embeddings**
- Train your own or download from the web.
- The example run uses ```embedding/bio_embedding.txt```, which can be downloaded from [our group's server](http://dmserv4.cs.illinois.edu/bio_embedding.txt). For example, ```curl http://dmserv4.cs.illinois.edu/bio_embedding.txt -o embedding/bio_embedding.txt```. Since the embedding encoding step consumes quite a lot of memory, we also provide the encoded file in the ```autoner_train.sh```.
- **[Optional]** Development & Test Sets.
- Example: ```data/BC5CDR/truth_dev.ck``` and ```data/BC5CDR/truth_test.ck```
- Three columns (i.e., token, ```Tie or Break``` label, entity type).
- ```I``` is ```Break```.
- ```O``` is ```Tie```.
- Two special tokens `````` and `````` mean the start and end of the sentence.
### Dependencies
This project is based on ```python>=3.6```. The dependent package for this project is listed as below:
```
numpy==1.13.1
tqdm
torch-scope>=0.5.0
pytorch==0.4.1
```
### Command
To train an AutoNER model, please run
```
./autoner_train.sh
```
To apply the trained AutoNER model, please run
```
./autoner_test.sh
```
You can specify the parameters in the bash files. The variables names are self-explained.
## Citation
Please cite the following two papers if you are using our tool. Thanks!
- Jingbo Shang*, Liyuan Liu*, Xiaotao Gu, Xiang Ren, Teng Ren and Jiawei Han, "**[Learning Named Entity Tagger using Domain-Specific Dictionary](https://arxiv.org/abs/1809.03599)**", in Proc. of 2018 Conf. on Empirical Methods in Natural Language Processing (EMNLP'18), Brussels, Belgium, Oct. 2018. (* Equal Contribution)
- Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R Voss, Jiawei Han, "**[Automated Phrase Mining from Massive Text Corpora](https://arxiv.org/abs/1702.04457)**", accepted by IEEE Transactions on Knowledge and Data Engineering, Feb. 2018.
```
@inproceedings{shang2018learning,
title = {Learning Named Entity Tagger using Domain-Specific Dictionary},
author = {Shang, Jingbo and Liu, Liyuan and Ren, Xiang and Gu, Xiaotao and Ren, Teng and Han, Jiawei},
booktitle = {EMNLP},
year = 2018,
}
@article{shang2018automated,
title = {Automated phrase mining from massive text corpora},
author = {Shang, Jingbo and Liu, Jialu and Jiang, Meng and Ren, Xiang and Voss, Clare R and Han, Jiawei},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {2018},
publisher = {IEEE}
}
```