Named Entity Recognition

Named entity recognition (NER) is a fundamental task that aims to identify named entities in raw text and assign them pre-defined categorical tags such as PER (Person), ORG (Organization), LOC (Location), etc.
This is an implementation of the paper: Attention-based Multi-level Feature Fusion for Named Entity Recognition. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. This study proposes a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER.

Requirements

• Ubuntu
• Python 3.6.9+
• TensorFlow-gpu 1.13.1
• CUDA 10+
• pathlib
• numpy
• json

Datasets

• CoNLL-2003
• NCBI-disease
• SciERC
• JNLPBA

Usage

1. Switch to the corresponding virtual environment, and install metrics for NER

    pip install git+https://github.com/guillaumegenthial/tf_metrics.git

2. Put the dataset into the corresponding directory and preprocess the datasets to the CONLL format, e.g.,
   

    data/sample

3. get pre-trained word embeddings GloVe and put it into data/sample;
   

Switch to ../data/sample:
Run preprocess.py and make sure the first line of the output file is not blank.

      python preprocess.py

decompress glove:

      unzip glove.840B.300d.zip -d glove.840B.300d.txt
      rm glove.840B.300d.zip

build vocab and glove:

      python build_vocab.py
      python build_glove.py

4. Switch to the root directory and get started with main_amff.py.
   

      python main_amff.py

References

• Scibert: A pretrained language model for scientific text， Beltagy et al. link
• Collabonet: collaboration of deep neural networks for biomedical named entity recognition, Yoon, et al. link
• Contextual String Embeddings for Sequence Labeling, Akbik et al. link
• Neural architectures for named entity recognition, Lample, et al. link
• End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF, Ma et al. link
• Named Entity Recognition with Bidirectional LSTM-CNNs, JPC Chiu et al. link
• Bidirectional LSTM-CRF Models for Sequence Tagging, Z Huang et al. link
• Sequence-tagging-with-tensorflow, guillaumegenthial. link
  ...

Citation

Please cite:

@InProceedings{yang2020AMFF,
  title     = {Attention-based Multi-level Feature Fusion for Named Entity Recognition},
  author    = {Zhiwei Yang, Hechang Chen, Jiawei Zhang, Jing Ma, and Yi Chang},
  booktitle = {IJCAI},  
  year      = {2020},
 }