Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

Overview

TopClus

The source code used for Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations, published in WWW 2022.

Requirements

At least one GPU is required to run the code.

Before running, you need to first install the required packages by typing following commands (Using a virtual environment is recommended):

pip3 install -r requirements.txt

You need to also download the following resources in NLTK:

import nltk
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('universal_tagset')

Overview

TopClus is an unsupervised topic discovery method that jointly models words, documents and topics in a latent spherical space derived from pretrained language model representations.

Running Topic Discovery

The entry script is src/trainer.py and the meanings of the command line arguments will be displayed upon typing

python src/trainer.py -h

The topic discovery results will be written to results_${dataset}.

We provide two example scripts nyt.sh and yelp.sh for running topic discovery on the New York Times and the Yelp Review corpora used in the paper, respectively. You need to first extract the text files from the .tar.gz tarball files under datasets/nyt and datasets/yelp.

You could expect to obtain results like the following (the Topic IDs are random):

On New York Times:
Topic 20: months,weeks,days,decades,years,hours,decade,seconds,moments,minutes
Topic 28: weapons,missiles,missile,nuclear,grenades,explosions,explosives,launcher,bombs,bombing
Topic 30: healthcare,medical,medicine,physicians,patients,health,hospitals,bandages,medication,physician
Topic 41: economic,commercially,economy,business,industrial,industry,market,consumer,trade,commerce
Topic 46: senate,senator,congressional,legislators,legislatures,ministry,legislature,minister,ministerial,parliament
Topic 72: government,administration,governments,administrations,mayor,gubernatorial,mayoral,mayors,public,governor
Topic 77: aircraft,airline,airplane,airlines,voyage,airplanes,aviation,planes,spacecraft,flights
Topic 88: baseman,outfielder,baseball,innings,pitchers,softball,inning,basketball,shortstop,pitcher
On Yelp Review:
Topic 1: steamed,roasted,fried,shredded,seasoned,sliced,frozen,baked,canned,glazed
Topic 15: nice,cozy,elegant,polite,charming,relaxing,enjoyable,pleasant,helpful,luxurious
Topic 16: spicy,fresh,creamy,stale,bland,salty,fluffy,greasy,moist,cold
Topic 17: flavor,texture,flavors,taste,quality,smells,tastes,flavour,scent,ingredients
Topic 20: japanese,german,australian,moroccan,russian,greece,italian,greek,asian,
Topic 40: drinks,beers,beer,wine,beverages,alcohol,beverage,vodka,champagne,wines
Topic 55: horrible,terrible,shitty,awful,dreadful,worst,worse,disgusting,filthy,rotten
Topic 75: strawberry,berry,onion,peppers,tomato,onions,potatoes,vegetable,mustard,garlic

Running Document Clustering

The latent document embeddings will be saved to results_${dataset}/latent_doc_emb.pt which can be used as features to clustering algorithms (e.g., K-Means).

If you have ground truth document labels, you could obtain the document clustering evaluation results by passing the document label file and the saved latent document embedding file to the cluster_eval function in src/utils.py. For example:

from src.utils import TopClusUtils
utils = TopClusUtils()
utils.cluster_eval(label_path="datasets/nyt/label_topic.txt", emb_path="results_nyt/latent_doc_emb.pt")

Running on New Datasets

To execute the code on a new dataset, you need to

  1. Create a directory named your_dataset under datasets.
  2. Prepare a text corpus texts.txt (one document per line) under your_dataset as the target corpus for topic discovery.
  3. Run src/trainer.py with appropriate command line arguments (the default values are usually good start points).

Citations

Please cite the following paper if you find the code helpful for your research.

@inproceedings{meng2022topic,
  title={Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations},
  author={Meng, Yu and Zhang, Yunyi and Huang, Jiaxin and Zhang, Yu and Han, Jiawei},
  booktitle={The Web Conference},
  year={2022},
}
Owner
Yu Meng
Ph.D. student, Text Mining
Yu Meng
Weighted K Nearest Neighbors (kNN) algorithm implemented on python from scratch.

kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new

1 Dec 14, 2021
Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"

ON-LSTM This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree

Yikang Shen 572 Nov 21, 2022
Cross-Task Consistency Learning Framework for Multi-Task Learning

Cross-Task Consistency Learning Framework for Multi-Task Learning Tested on numpy(v1.19.1) opencv-python(v4.4.0.42) torch(v1.7.0) torchvision(v0.8.0)

Aki Nakano 2 Jan 08, 2022
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 02, 2022
Predict halo masses from simulations via graph neural networks

HaloGraphNet Predict halo masses from simulations via Graph Neural Networks. Given a dark matter halo and its galaxies, creates a graph with informati

Pablo Villanueva Domingo 20 Nov 15, 2022
git git《Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking》(CVPR 2021) GitHub:git2] 《Masksembles for Uncertainty Estimation》(CVPR 2021) GitHub:git3]

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li Accepted by CVPR

NingWang 236 Dec 22, 2022
Reproduce ResNet-v2(Identity Mappings in Deep Residual Networks) with MXNet

Reproduce ResNet-v2 using MXNet Requirements Install MXNet on a machine with CUDA GPU, and it's better also installed with cuDNN v5 Please fix the ran

Wei Wu 531 Dec 04, 2022
DecoupledNet is semantic segmentation system which using heterogeneous annotations

DecoupledNet: Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Created by Seunghoon Hong, Hyeonwoo Noh and Bohyung Han at POSTE

Hyeonwoo Noh 74 Sep 22, 2021
Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow

Perceiver This Python package implements Perceiver: General Perception with Iterative Attention by Andrew Jaegle in TensorFlow. This model builds on t

Rishit Dagli 84 Oct 15, 2022
A embed able annotation tool for end to end cross document co-reference

CoRefi CoRefi is an emebedable web component and stand alone suite for exaughstive Within Document and Cross Document Coreference Anntoation. For a de

PythicCoder 39 Dec 12, 2022
Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

PyTorch Image Classifier Updates As for many users request, I released a new version of standared pytorch immage classification example at here: http:

JinTian 106 Nov 06, 2022
iris - Open Source Photos Platform Powered by PyTorch

Open Source Photos Platform Powered by PyTorch. Submission for PyTorch Annual Hackathon 2021.

Omkar Prabhu 137 Sep 10, 2022
Trainable Bilateral Filter Layer (PyTorch)

Trainable Bilateral Filter Layer (PyTorch) This repository contains our GPU-accelerated trainable bilateral filter layer (three spatial and one range

FabianWagner 26 Dec 25, 2022
Data-depth-inference - Data depth inference with python

Welcome! This readme will guide you through the use of the code in this reposito

Marco 3 Feb 08, 2022
Evaluation suite for large-scale language models.

This repo contains code for running the evaluations and reproducing the results from the Jurassic-1 Technical Paper (see blog post), with current support for running the tasks through both the AI21 S

71 Dec 17, 2022
Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".

Topology-Imbalance Learning for Semi-Supervised Node Classification Introduction Code for NeurIPS 2021 paper "Topology-Imbalance Learning for Semi-Sup

Victor Chen 40 Nov 23, 2022
A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CLEVR Dataset Generation This is the code used to generate the CLEVR dataset as described in the paper: CLEVR: A Diagnostic Dataset for Compositional

Facebook Research 503 Jan 04, 2023
Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

Mozhdeh Gheini 16 Jul 16, 2022
[ICLR 2021] Is Attention Better Than Matrix Decomposition?

Enjoy-Hamburger 🍔 Official implementation of Hamburger, Is Attention Better Than Matrix Decomposition? (ICLR 2021) Under construction. Introduction T

Gsunshine 271 Dec 29, 2022
Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

53 Nov 22, 2022