Negative sampling for solving the unlabeled entity problem in NER. ICLR-2021 paper: Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition.

Related tags

Text Data & NLP NegSampling-NER
Overview

Negative Sampling for NER

Unlabeled entity problem is prevalent in many NER scenarios (e.g., weakly supervised NER). Our paper in ICLR-2021 proposes using negative sampling for solving this important issue. This repo. contains the implementation of our approach.

Note that this is not an officially supported Tencent product.

Preparation

Two steps. Firstly, reformulate the NER data and move it into a new folder named "dataset". The folder contains {train, dev, test}.json. Each JSON file is a list of dicts. See the following case:

[ 
 {
  "sentence": "['Somerset', '83', 'and', '174', '(', 'P.', 'Simmons', '4-38', ')', ',', 'Leicestershire', '296', '.']",
  "labeled entities": "[(0, 0, 'ORG'), (5, 6, 'PER'), (10, 10, 'ORG')]",
 },
 {
  "sentence": "['Leicestershire', '22', 'points', ',', 'Somerset', '4', '.']",
  "labeled entities": "[(0, 0, 'ORG'), (4, 4, 'ORG')]",
 }
]

Secondly, pretrained LM (i.e., BERT) and eval. script. Create a dir. named "resource" and arrange them as

  • resource
    • bert-base-cased
      • model.pt
      • vocab.txt
    • conlleval.pl

Note that the files in BERT.tar.gz need to be renamed as above.

Training and Test

CUDA_VISIBLE_DEVICES=0 python main.py -dd dataset -cd save -rd resource

Citation

@inproceedings{li2021empirical,
    title={Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition},
    author={Yangming Li and lemao liu and Shuming Shi},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=5jRVa89sZk}
}
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Comments
  • hyperparameter settings for reproducing results

    hyperparameter settings for reproducing results

    Hi,

    Interesting work! I am trying to reproduce your experiments was hoping you could answer a few questions:

    • Could you please list any hyperparameter settings/commands you used for the conll2003 and ontonotes5 evaluations?
    • I've found that default batch size is too large and I am getting CUDA OOM issues. What batch size did you use? (My GPU has 16gb ram, so I was also wondering what the gpu capacity was for the machines used to run the published experiments?)
    • For ontonotes, some of the sentences are too long to fit into bert, did you just truncate these?

    Thanks!

    opened by teffland 1
  • Not a gzip file

    Not a gzip file

    I have arranged the folders as stated in the repo. But i hav received this error.

    1649, in gzopen raise ReadError("not a gzip file") tarfile.ReadError: not a gzip file

    opened by zhaozhijie1997 0
  • 关于run的一点小问题？

    关于run的一点小问题？

    按照Readme操作后出现此问题，请问应该怎么解决 image train、dev、test数据用的都是示例提供的数据

    [ 
 {
  "sentence": "['Somerset', '83', 'and', '174', '(', 'P.', 'Simmons', '4-38', ')', ',', 'Leicestershire', '296', '.']",
  "labeled entities": "[(0, 0, 'ORG'), (5, 6, 'PER'), (10, 10, 'ORG')]",
 },
 {
  "sentence": "['Leicestershire', '22', 'points', ',', 'Somerset', '4', '.']",
  "labeled entities": "[(0, 0, 'ORG'), (4, 4, 'ORG')]",
 }
]
    opened by FalAnge1217 0
Owner
Yangming Li
Researcher at Tencent AI Lab.
Yangming Li
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