Preprocessed Datasets for our Multimodal NER paper

Related tags

Deep Learning UMT
Overview

Unified Multimodal Transformer (UMT) for Multimodal Named Entity Recognition (MNER)

Two MNER Datasets and Codes for our ACL'2020 paper: Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer.

Author

Jianfei Yu

[email protected]

July 1, 2020

Data

Requirement

  • PyTorch 1.0.0
  • Python 3.7

Code Usage

Training for UMT

  • This is the training code of tuning parameters on the dev set, and testing on the test set. Note that you can change "CUDA_VISIBLE_DEVICES=2" based on your available GPUs.
sh run_mtmner_crf.sh
  • We show our running logs on twitter-2015 and twitter-2017 in the folder "log files". Note that the results are a little bit lower than the results reported in our paper, since the experiments were run on different servers.

Acknowledgements

  • Using these two datasets means you have read and accepted the copyrights set by Twitter and dataset providers.
Comments
  • the problems of ESD

    the problems of ESD

    Hey,i am attracted to the code of your paper,and would like to ask how the 'ESD Module' and 'Conversion Matrix' are implemented in the paper? which part of the code it correspond to?Look forward to your reply.

    opened by goodbai2 1
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
  • About number of entites in dataset

    About number of entites in dataset

    First, thank you for your excellent work! When I run your mode on Twitter2015, I noticed the eval result is below: precision recall f1-score support

         LOC     0.7721    0.8471    0.8079      1720
    MISC     0.3599    0.4072    0.3821       754
     ORG     0.6380    0.5860    0.6109       860
     PER     0.8363    0.8783    0.8568      1873
       _     0.0000    0.0000    0.0000         0

    Please attend to the support column, num of entites does not match the description of dataset Twitter2015. For instance, here the num of PER entites is 1873 in dev set, while description of dataset Twitter2015 says the num of PER entites in dev set is 1816. I cannot understand why there can be more entities reported in eval result. And I sincerely ask for your help. Thanks Again :)

    opened by gagaein 2
  • 关于两个数据集的问题

    关于两个数据集的问题

    你好，在您的论文中，您使用了TWITTER-2015和TWITTER-2017这两个数据集，分别由di lu 和 zhang qi 构建。然而我读了他们的论文，却没有在论文里找到他们提供的数据集链接。 我这里想复现他们的论文，请问你是从哪里得到他们的数据集的，如果他们还公布了具体实现的代码，请问可以分享一下吗。 非常感谢！！！

    opened by JinFish 0
  • About Twitter-2017

    About Twitter-2017

    Hi,

    In the paper, the Twitter 2017 datasets contain 3373/723/723 sentences for train/dev/test respectively. However, in the paper of Lu et al., 2018, they reported that there are 4290/1432/1459 sentences in the Twitter dataset. Could you tell me what is the difference between the two datasets?

    opened by wangxinyu0922 0
  • Question of the MT-bert code

    Question of the MT-bert code

    Dear Jeffery，I'm very interested in your work of this paper.I have a little problem about the code.If I want to implement MT-BERT-CRF, which part of the code should I modify?Thank u.

    opened by blink7-lab 6
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