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
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation

Inverse Q-Learning (IQ-Learn) Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight IQ-Learn is an easy-to-use

Divyansh Garg 102 Dec 20, 2022
MVP Benchmark for Multi-View Partial Point Cloud Completion and Registration

MVP Benchmark: Multi-View Partial Point Clouds for Completion and Registration [NEWS] 2021-07-12 [NEW 🎉 ] The submission on Codalab starts! 2021-07-1

PL 93 Dec 21, 2022
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator This is the official code repository for NeurIPS 2021 paper: CARMS: Categorica

Alek Dimitriev 1 Jul 09, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

57 Nov 28, 2022
Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Dominik Klein 189 Dec 21, 2022
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

Jittor: a Just-in-time(JIT) deep learning framework Quickstart | Install | Tutorial | Chinese Jittor is a high-performance deep learning framework bas

2.7k Jan 03, 2023
Kalidokit is a blendshape and kinematics solver for Mediapipe/Tensorflow.js face, eyes, pose, and hand tracking models

Blendshape and kinematics solver for Mediapipe/Tensorflow.js face, eyes, pose, and hand tracking models.

Rich 4.5k Jan 07, 2023
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

Scalable Incomplete Network Embedding ⠀⠀ A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network em

Benedek Rozemberczki 69 Sep 22, 2022
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
Sound Source Localization for AI Grand Challenge 2021

Sound-Source-Localization Sound Source Localization study for AI Grand Challenge 2021 (sponsored by NC Soft Vision Lab) Preparation 1. Place the data-

sanghoon 19 Mar 29, 2022
VQGAN+CLIP Colab Notebook with user-friendly interface.

VQGAN+CLIP and other image generation system VQGAN+CLIP Colab Notebook with user-friendly interface. Latest Notebook: Mse regulized zquantize Notebook

Justin John 227 Jan 05, 2023
내가 보려고 정리한 <프로그래밍 기초 Ⅰ> / organized for me

Programming-Basics 프로그래밍 기초 Ⅰ 아카이브 Do it! 점프 투 파이썬 주차 강의주제 비고 1주차 Syllabus 2주차 자료형 - 숫자형 3주차 자료형 - 문자열형 4주차 입력과 출력 5주차 제어문 - 조건문 if 6주차 제어문 - 반복문 whil

KIMMINSEO 1 Mar 07, 2022
OCR-D wrapper for detectron2 based segmentation models

ocrd_detectron2 OCR-D wrapper for detectron2 based segmentation models Introduction Installation Usage OCR-D processor interface ocrd-detectron2-segm

Robert Sachunsky 13 Dec 06, 2022
Self-supervised spatio-spectro-temporal represenation learning for EEG analysis

EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation This repository provides a tensorflow implementation of a submitted paper: EEG-Orie

Wonjun Ko 4 Jun 09, 2022
The code for MM2021 paper "Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning"

The Code for MM2021 paper "Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning" Setting up and using the repo Get the dataset. Follow

4 Apr 20, 2022
We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC).

EMTAUC We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC). In this code, SBGA is considered a ba

7 Nov 24, 2022
GAN-based 3D human pose estimation model for 3DV'17 paper

Tensorflow implementation for 3DV 2017 conference paper "Adversarially Parameterized Optimization for 3D Human Pose Estimation". @inproceedings{jack20

Dominic Jack 15 Feb 27, 2021
Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Sifei Liu 167 Dec 14, 2022
Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch

MeMOT - Pytorch (wip) Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch. This paper is just one in a line of work, but importan

Phil Wang 15 May 09, 2022