Explainable Zero-Shot Topic Extraction

Related tags

Deep LearningZeSTE
Overview

Zero-Shot Topic Extraction with Common-Sense Knowledge Graph

This repository contains the code for reproducing the results reported in the paper "Explainable Zero-Shot Topic Extraction with Common-Sense Knowledge Graph" (pdf) at the LDK 2021 Conference.

A user-friendly demo is available at: http://zeste.tools.eurecom.fr/

ZeSTE

Based on ConceptNet's common sense knowledge graph and embeddings, ZeSTE generates explainable predictions for a document topical category (e.g. politics, sports, video_games ..) without reliance on training data. The following is a high-level illustration of the approach:

API

ZeSTE can also be accessed via a RESTful API for easy deployment and use. For further information, please refer to the documentation: https://zeste.tools.eurecom.fr/doc

Dependencies

Before running any code in this repo, please install the following dependencies:

  • numpy
  • pandas
  • matplotlib
  • nltk
  • sklearn
  • tqdm
  • gensim

Code Overview

This repo is organized as follows:

  • generate_cache.py: this script processes the raw ConceptNet dump to produce cached files for each node in ConceptNet to accelerate the label neighborhood generation. It also transforms ConceptNet Numberbatch text file into a Gensim word embedding that we pickle for quick loading.
  • zeste.py: this is the main script for evaluation. It takes as argument the dataset to process as well as model configuration parameters such as neighborhood depth (see below). The results (classification report, confusion matrix, and classification metrics) are persisted into text files.
  • util.py: contains the functions that are used in zeste.py
  • label_mappings: contains the tab-separated mappings for the studied datasets.

Reproducing Results

1. Downloads

The two following files need to be downloaded to bypass the use of ConceptNet's web API: the dump of ConceptNet triplets, and the ConceptNet Numberbatch pre-computed word embeddings. You can download them from ConceptNet's and Numberbatch's repos, respectively.

# wget https://s3.amazonaws.com/conceptnet/downloads/2019/edges/conceptnet-assertions-5.7.0.csv.gz
# wget https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-19.08.txt.gz
# gzip -d conceptnet-assertions-5.7.0.csv.gz
# gzip -d numberbatch-19.08.txt.gz

2. generate_cache.py

This script takes as input the two just-downloaded files and the cache path to where precomputed 1-hop label neighborhoods will be saved. This can take up to 7.2G of storage space.

usage: generate_cache.py [-h] [-cnp CONCEPTNET_ASSERTIONS_PATH] [-nbp CONCEPTNET_NUMBERBATCH_PATH] [-zcp ZESTE_CACHE_PATH]

Zero-Shot Topic Extraction

optional arguments:
  -h, --help            show this help message and exit
  -cnp CONCEPTNET_ASSERTIONS_PATH, --conceptnet_assertions_path CONCEPTNET_ASSERTIONS_PATH
                        Path to CSV file containing ConceptNet assertions dump
  -nbp CONCEPTNET_NUMBERBATCH_PATH, --conceptnet_numberbatch_path CONCEPTNET_NUMBERBATCH_PATH
                        Path to W2V file for ConceptNet Numberbatch
  -zcp ZESTE_CACHE_PATH, --zeste_cache_path ZESTE_CACHE_PATH
                        Path to the repository where the generated files will be saved

3. zeste.py

This script uses the precomputed 1-hop label neighborhoods to recursively generate label neighborhoods of any given depth (-d). It takes also as parameters the path to the dataset CSV file (which should have two columns: text and label). The rest of the arguments are for model experimentation.

usage: zeste.py [-h] [-cp CACHE_PATH] [-pp PREFETCH_PATH] [-nb NUMBERBATCH_PATH] [-dp DATASET_PATH] [-lm LABELS_MAPPING] [-rp RESULTS_PATH]
                [-d DEPTH] [-f FILTER] [-s {simple,compound,depth,harmonized}] [-ar ALLOWED_RELS]

Zero-Shot Topic Extraction

optional arguments:
  -h, --help            show this help message and exit
  -cp CACHE_PATH, --cache_path CACHE_PATH
                        Path to where the 1-hop word neighborhoods are cached
  -pp PREFETCH_PATH, --prefetch_path PREFETCH_PATH
                        Path to where the precomputed n-hop neighborhoods are cached
  -nb NUMBERBATCH_PATH, --numberbatch_path NUMBERBATCH_PATH
                        Path to the pickled Numberbatch
  -dp DATASET_PATH, --dataset_path DATASET_PATH
                        Path to the dataset to process
  -lm LABELS_MAPPING, --labels_mapping LABELS_MAPPING
                        Path to the mapping between the dataset labels and ZeSTE labels (multiword labels are comma-separated)
  -rp RESULTS_PATH, --results_path RESULTS_PATH
                        Path to the directory where to store the results
  -d DEPTH, --depth DEPTH
                        How many hops to generate the neighborhoods
  -f FILTER, --filter FILTER
                        Filtering method: top[N], top[P]%, thresh[T], all
  -s {simple,compound,depth,harmonized}, --similarity {simple,compound,depth,harmonized}
  -ar ALLOWED_RELS, --allowed_rels ALLOWED_RELS
                        Which relationships to use (comma-separated or all)

Cite this work

@InProceedings{harrando_et_al_zeste_2021,
  author ={Harrando, Ismail and Troncy, Rapha\"{e}l},
  title ={{Explainable Zero-Shot Topic Extraction Using a Common-Sense Knowledge Graph}},
  booktitle ={3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages ={17:1--17:15},
  year ={2021},
  volume ={93},
  publisher ={Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  URL ={https://drops.dagstuhl.de/opus/volltexte/2021/14553},
  URN ={urn:nbn:de:0030-drops-145532},
  doi ={10.4230/OASIcs.LDK.2021.17},
}
Owner
D2K Lab
Data to Knowledge Virtual Lab (LINKS Foundation - EURECOM)
D2K Lab
GitHub Repository
SuRE Evaluation: A Supplementary Material

SuRE Evaluation: A Supplementary Material This repository contains supplementary material regarding the evaluations presented in the paper Visual Expl

NYU Visualization Lab 0 Dec 14, 2021
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

Yuan Yin 24 Oct 24, 2022
Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom

Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom Sample on-line plotting while training(avg loss)/testing(writ

Jingwei Zhang 269 Nov 15, 2022
Code for "Adversarial attack by dropping information." (ICCV 2021)

AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa

Ranjie Duan 52 Nov 10, 2022
ExCon: Explanation-driven Supervised Contrastive Learning

ExCon: Explanation-driven Supervised Contrastive Learning Link to the paper: https://arxiv.org/pdf/2111.14271.pdf Contributors of this repo: Zhibo Zha

Zhibo (Darren) Zhang 18 Nov 01, 2022
[ACM MM 2021] Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)

Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation) [arXiv] [paper] @inproceedings{hou2021multiview, title={Multiview

Yunzhong Hou 27 Dec 13, 2022
(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

IsoTree Fast and multi-threaded implementation of Extended Isolation Forest, Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and regular

141 Dec 29, 2022
MOpt-AFL provided by the paper "MOPT: Optimized Mutation Scheduling for Fuzzers"

MOpt-AFL 1. Description MOpt-AFL is a AFL-based fuzzer that utilizes a customized Particle Swarm Optimization (PSO) algorithm to find the optimal sele

172 Dec 18, 2022
Space Time Recurrent Memory Network - Pytorch

Space Time Recurrent Memory Network - Pytorch (wip) Implementation of Space Time Recurrent Memory Network, recurrent network competitive with attentio

Phil Wang 50 Nov 07, 2021
H&M Fashion Image similarity search with Weaviate and DocArray

H&M Fashion Image similarity search with Weaviate and DocArray This example shows how to do image similarity search using DocArray and Weaviate as Doc

Laura Ham 18 Aug 11, 2022
Efficiently Disentangle Causal Representations

Efficiently Disentangle Causal Representations Install dependency pip install -r requirements.txt Main experiments Causality direction prediction cd

4 Apr 01, 2022
Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision

MLP-Mixer: An all-MLP Architecture for Vision This repo contains PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision. Usage : impo

Rishikesh (ऋषिकेश) 175 Dec 23, 2022
Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models

merged_depth runs (1) AdaBins, (2) DiverseDepth, (3) MiDaS, (4) SGDepth, and (5) Monodepth2, and calculates a weighted-average per-pixel absolute dept

Pranav 39 Nov 21, 2022
CNNs for Sentence Classification in PyTorch

Introduction This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch. Kim's implementation of t

Shawn Ng 956 Dec 19, 2022
Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Ankou Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering t

SoftSec Lab 54 Dec 24, 2022
Recurrent Conditional Query Learning

Recurrent Conditional Query Learning (RCQL) This repository contains the Pytorch implementation of One Model Packs Thousands of Items with Recurrent C

Dongda 4 Nov 28, 2022
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy

George Cazenavette 256 Jan 05, 2023
An efficient PyTorch implementation of the evaluation metrics in recommender systems.

recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark

Xingdong Zuo 12 Dec 02, 2022
PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot

Progressive Growing of GANs inference in PyTorch with CelebA training snapshot Description This is an inference sample written in PyTorch of the origi

320 Nov 21, 2022
The final project of "Applying AI to 3D Medical Imaging Data" from "AI for Healthcare" nanodegree - Udacity.

Quantifying Hippocampus Volume for Alzheimer's Progression Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder that result

Omar Laham 1 Jan 14, 2022