SeLaB: Semantic Labeling with BERT

This repository contains source code for the SeLaB model (IJCNN, arXiv), a new context-aware semantic labeling method using both the column values and contextual information. SeLaB s based on a new setting for semantic labeling, where we sequentially predict labels for an input table with missing headers. We incorporate both the values and context of each data column using the pre-trained contextualized language model, BERT. To our knowledge, we are the first to successfully apply BERT to solve the semantic labeling task.

Installation

First, install the conda environment selab with supporting libraries.

conda create --name selab python=3.7
conda activate selab
pip install -r requirements.txt

Data

We use WikiTables collection for evaluation. This dataset is composed of the WikiTables corpus which contains over 1.6𝑀 tables that are extracted from Wikipedia. Since a lot of tables have unexpected formats, we preprocess tables so that we only keep tables that have enough content with at least 3 columns and 50 rows. We further filter the columns whose schema labels appear less than 10 times in the table corpus, as there are not enough data tables that can be used to train the model to recognize these labels. We experiment on 15, 252 data tables, with a total number of columns equal to 82, 981. The total number of schema labels is equal to 1088. The dataset could be downloaded from this Google Drive shared folder. Please unzip the downloaded file, and place it in the foler data.

Training with SeLaB

To train the semantic labeling model with SeLaB:

Split the data to train and test splits:

python train_test_split.py

Train SeLaB model with the default parameters

python selab_train.py

Test SeLaB model with the default parameters:

python selab_test.py

Reference

If you plan to use SeLaB in your project, please consider citing our paper:

@INPROCEEDINGS{trabelsi21ijcnn,
  author={Trabelsi, Mohamed and Cao, Jin and Heflin, Jeff},
  booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, 
  title={SeLaB: Semantic Labeling with BERT}, 
  year={2021},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/IJCNN52387.2021.9534408}}

Contact

if you have any questions, please contact Mohamed Trabelsi at mot218@lehigh.edu