Each Dataset should contain following files:
- DataFrame pickle file
- Example:
data/nyt/coarse/df.pkl- This dataset should contain two columns named
sentence,label sentencecontains text andlabelcontains its corresponding label.- Must be named as
df.pkl
- This dataset should contain two columns named
- Example:
- Seed Words Json file
- Example:
data/nyt/coarse/seedwords.json- This json file contains seed words list for each label.
- Must be named as
seedwords.json
- Example:
The contextualize.py requires three arguments: dataset_path, which is a path to dataset containing
required DataFrame and seedwords, temp_dir is a path to a temporary
directory which is used for dumping intermediate files during contextualizing the corpus, and gpu_id refers to the
id of the gpu. If not mentioned, the process runs on cpu.
To contextualize the corpus, please run:
$ python contextualize.py --dataset_path dataset_path --temp_dir temp_dir_path --gpu_id 3The tests/test_contextualize.py is a unittest to check the sanity of contextualization. To run this unittest, please execute:
$ python -m unittest tests/test_contextualize.pyThe train.py requires two arguments: dataset_path, which is a path to dataset containing
required contextualized corpus DataFrame dumped by contextualize.py and seed words, and gpu_id refers to the
id of the gpu. If not mentioned,
the process runs on cpu.
To train ConWea, please run:
$ python train.py --dataset_path dataset_path --gpu_id 3The tests/test_conwea.py is a unittest to check the sanity of framework. To run this unittest, please execute:
$ python -m unittest tests/test_conwea.pyThis project is based on python==3.7. The dependencies are as follow:
keras-contrib==2.0.8
scikit-learn==0.21.3
flair==0.4.4
scipy=1.3.1
gensim==3.8.1
numpy==1.17.2
@inproceedings{mekala-shang-2020-contextualized,
title = "Contextualized Weak Supervision for Text Classification",
author = "Mekala, Dheeraj and
Shang, Jingbo",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.30",
pages = "323--333",
abstract = "Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers. Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlooked. In this paper, we propose a novel framework ConWea, providing contextualized weak supervision for text classification. Specifically, we leverage contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of the same word, and thus create a contextualized corpus. This contextualized corpus is further utilized to train the classifier and expand seed words in an iterative manner. This process not only adds new contextualized, highly label-indicative keywords but also disambiguates initial seed words, making our weak supervision fully contextualized. Extensive experiments and case studies on real-world datasets demonstrate the necessity and significant advantages of using contextualized weak supervision, especially when the class labels are fine-grained.",
}
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