Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

Overview

Hierarchical Metadata-Aware Document Categorization under Weak Supervision

This project provides a weakly supervised framework for hierarchical metadata-aware document categorization.

Links

Installation

For training, a GPU is strongly recommended.

Keras

The code is based on Keras. You can find installation instructions here.

Dependency

The code is written in Python 3.6. The dependencies are summarized in the file requirements.txt. You can install them like this:

pip3 install -r requirements.txt

Quick Start

To reproduce the results in our paper, you need to first download the datasets. Three datasets are used in our paper: GitHub, ArXiv, and Amazon. Once you unzip the downloaded file (i.e., data.zip), you can see three folders related to these three datasets, respectively.

Dataset #Documents #Layers #Classes (including ROOT) #Leaves Sample Classes
GitHub 1,596 2 18 14 Computer Vision (Layer-1), Image Generation (Layer-2)
ArXiv 26,400 2 94 88 cs (Layer-1), cs.AI (Layer-2)
Amazon 147,000 2 166 147 Automotive (Layer-1), Car Care (Layer-2)

You need to put these 3 folders under the main folder ./. Then the following running script can be used to run the model.

./test.sh

Level-1/Level-2/Overall Micro-F1/Macro-F1 scores will be shown in the last several lines of the output. The classification result can be found under your dataset folder. For example, if you are using the GitHub dataset, the output will be ./github/out.txt.

Data

In each of the three folders (i.e., github/, arxiv/, and amazon/), there is a json file, where each line represents one document with text and metadata information.

For GitHub, the json format is

{
  "id": "Natsu6767/DCGAN-PyTorch",  
  "user": [
    "Natsu6767"
  ],
  "text": "pytorch implementation of dcgan trained on the celeba dataset deep convolutional gan ...",
  "tags": [
    "pytorch",
    "dcgan",
    "gan",
    "implementation",
    "deeplearning",
    "computer-vision",
    "generative-model"
  ],
  "labels": [
    "$Computer-Vision",
    "$Image-Generation"
  ]
}

The "user" and "tags" fields are metadata.

For ArXiv, the json format is

{
  "id": "1001.0063",
  "authors": [
    "Alessandro Epasto",
    "Enrico Nardelli"
  ],
  "text": "on a model for integrated information in this paper we give a thorough presentation ...",
  "labels": [
    "cs",
    "cs.AI"
  ]
}

The "authors" field is metadata.

For Amazon, the json format is

{
  "user": [
    "A39IXH6I0WT6TK"
  ],
  "product": [
    "B004DLPXAO"
  ],
  "text": "works really great only had a problem when it was updated but they fixed it right away ...",
  "labels": [
    "Apps-for-Android",
    "Books-&-Comics"
  ]
}

The "user" and "product" fields are metadata.

NOTE 1: If you would like to run our code on your own dataset, when you prepare this json file, make sure that: (1) You list the labels in the top-down order. For example, if the label path of your repository is ROOT-A-B-C, then the "labels" field should be ["A", "B", "C"]. (2) For each document, its metadata field is always represented by a list. For example, the "user" field should be ["A39IXH6I0WT6TK"] instead of "A39IXH6I0WT6TK".

Running on New Datasets

In the Quick Start section, we include a pretrained embedding file in the downloaded folders. If you would like to re-train the embedding (or you have a new dataset), please follow the steps below.

  1. Create a directory named ${dataset} under the main folder (e.g., ./github).

  2. Prepare four files:
    (1) ./${dataset}/label_hier.txt indicating the parent children relationships between classes. The first class of each line is the parent class, followed by all its children classes. Whitespace is used as the delimiter. The root class must be named as ROOT. Make sure your class names do not contain whitespace.
    (2) ./${dataset}/doc_id.txt containing labeled document ids for each class. Each line begins with the class name, and then document ids in the corpus (starting from 0) of the corresponding class separated by whitespace.
    (3) ./${dataset}/${json-name}.json. You can refer to the provided json format above. Make sure it has two fields "text" and "labels". You can add your own metadata fields in the json.
    (4) ./${dataset}/meta_dict.json indicating the names of your metadata fields. For example, for GitHub, it should be

{"metadata": ["user", "tags"]}

For ArXiv, it should be

{"metadata": ["authors"]}
  1. Install the dependencies GSL and Eigen. For Eigen, we already provide a zip file JointEmbedding/eigen-3.3.3.zip. You can directly unzip it in JointEmbedding/. For GSL, you can download it here.

  2. ./prep_emb.sh. Make sure you change the dataset/json names. The embedding file will be saved to ./${dataset}/embedding_sph.

After that, you can train the classifier as mentioned in Quick Start (i.e., ./test.sh). Please always refer to the example datasets when adapting the code for a new dataset.

Citation

If you find the implementation useful, please cite the following paper:

@inproceedings{zhang2021hierarchical,
  title={Hierarchical Metadata-Aware Document Categorization under Weak Supervision},
  author={Zhang, Yu and Chen, Xiusi and Meng, Yu and Han, Jiawei},
  booktitle={WSDM'21},
  pages={770--778},
  year={2021},
  organization={ACM}
}
Owner
Yu Zhang
CS Ph.D. student at UIUC; Data Mining
Yu Zhang
A simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

This is a simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

crispengari 3 Jan 08, 2022
Scenarios, tutorials and demos for Autonomous Driving

The Autonomous Driving Cookbook (Preview) NOTE: This project is developed and being maintained by Project Road Runner at Microsoft Garage. This is cur

Microsoft 2.1k Jan 02, 2023
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora

VITA 64 Dec 08, 2022
An implementation of the AdaOPS (Adaptive Online Packing-based Search), which is an online POMDP Solver used to solve problems defined with the POMDPs.jl generative interface.

AdaOPS An implementation of the AdaOPS (Adaptive Online Packing-guided Search), which is an online POMDP Solver used to solve problems defined with th

9 Oct 05, 2022
A Pythonic library for Nvidia Codec.

A Pythonic library for Nvidia Codec. The project is still in active development; expect breaking changes. Why another Python library for Nvidia Codec?

Zesen Qian 12 Dec 27, 2022
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022
AI创造营 :Metaverse启动机之重构现世,结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

paddle-wechaty-Zodiac AI创造营 :Metaverse启动机之重构现世,结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人 12星座若穿越科幻剧,会拥有什么超能力呢?快来迎接你的专属超能力吧! 现在很多年轻人都喜欢看科幻剧,像是复仇者系列,里面有很多英雄、超

105 Dec 22, 2022
Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

Zero-shot-Fact-Verification-by-Claim-Generation This repository contains code and models for the paper: Zero-shot Fact Verification by Claim Generatio

Liangming Pan 47 Jan 01, 2023
Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022
Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

Box_Discretization_Network This repository is built on the pytorch [maskrcnn_benchmark]. The method is the foundation of our ReCTs-competition method

Yuliang Liu 266 Nov 24, 2022
Change Detection in SAR Images Based on Multiscale Capsule Network

SAR_CD_MS_CapsNet Code for the paper "Change Detection in SAR Images Based on Multiscale Capsule Network" , IEEE Geoscience and Remote Sensing Letters

Feng Gao 21 Nov 29, 2022
Vector.ai assignment

fabio-tests-nisargatman Low Level Approach: ###Tables: continents: id*, name, population, area, createdAt, updatedAt countries: id*, name, population,

Ravi Pullagurla 1 Nov 09, 2021
Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Accompanying code for the paper Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Kevin Wilkinghoff 6 Dec 01, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
This repository contains the code for the paper 'PARM: Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval' published at ECIR'22.

Paragraph Aggregation Retrieval Model (PARM) for Dense Document-to-Document Retrieval This repository contains the code for the paper PARM: A Paragrap

Sophia Althammer 33 Aug 26, 2022
This is Unofficial Repo. Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection (CVPR 2021)

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection This is a PyTorch implementation of the LipForensics paper. This is an U

Minha Kim 2 May 11, 2022
TensorFlow implementation of Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction)

Barlow-Twins-TF This repository implements Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction) in TensorFlow and demonstrat

Sayak Paul 36 Sep 14, 2022
Official DGL implementation of "Rethinking High-order Graph Convolutional Networks"

SE Aggregation This is the implementation for Rethinking High-order Graph Convolutional Networks. Here we show the codes for citation networks as an e

Tianqi Zhang (张天启) 32 Jul 19, 2022
Data augmentation for NLP, accepted at EMNLP 2021 Findings

AEDA: An Easier Data Augmentation Technique for Text Classification This is the code for the EMNLP 2021 paper AEDA: An Easier Data Augmentation Techni

Akbar Karimi 81 Dec 09, 2022
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

The SLIDE package contains the source code for reproducing the main experiments in this paper. Dataset The Datasets can be downloaded in Amazon-

Intel Labs 72 Dec 16, 2022