AutoNER

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

Model notes
Benchmarks
Training
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
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. 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 Berak.
  - O is Tie.
  - Two special tokens <s> and <eof> mean the start and end of the sentence.

Dependencies

The dependent package for this project is listed as below:

numpy==1.13.1
tqdm
torch-scope
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", 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", 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}
}

MingYates/AutoNER