Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too! (2020; Code for paper)

This repo fixes reproducibility problems with the implementation of the paper Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too! (Sia, Dalmia, and Mieke; EMNLP, 2020).

How to use the code

• Install requirements by pip install -r requirements.txt.
• Download pre-trained models, for example

wget https://figshare.com/ndownloader/files/10798046 -O GoogleNews-vectors-negative300.bin # small model
wget https://github.com/eyaler/word2vec-slim/raw/master/GoogleNews-vectors-negative300-SLIM.bin.gz # large model
gzip GoogleNews-vectors-negative300-SLIM.bin.gz

• To cluster the word embeddings to discover the latent topics, run python code/score.py <arguments>. The description of the arguments is given below:

Required:

--entities : The type of pre-trained word embedding you are clustering with
choices= word2vec, fasttext, glove, KG KG stands for your own set of embeddings

--entities_file: The file name contain the embeddings

--clustering_algo: The clustering algorithm to use
choices= KMeans, SPKMeans, GMM, KMedoids, Agglo, DBSCAN , Spectral, VMFM

--vocab: List of vocab files to use for tokenization, for example /usr/share/dict/words

Not Required:

--dataset: Dataset to test clusters against against
default = 20NG choices= 20NG, reuters

--preprocess: Cuttoff threshold for words to keep in the vocab based on frequency

--use_dims: Dimensions to scale with PCA (much be less than orginal dims)

--num_topics: List of number of topics to try default: 20

--doc_info: How to add document information choices= DUP, WGT

--rerank: Value used for reranking the words in a cluster
choices=tf, tfidf, tfdf

Example call: python3 code/score.py --clustering_algo KMeans --vocab /usr/share/dict/words --entities word2vec

How to cite

To cite the original paper and this fork, use

@inproceedings{sia-etal-2020-tired,
    title = "Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!",
    author = "Sia, Suzanna  and
      Dalmia, Ayush  and
      Mielke, Sabrina J.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.135",
    doi = "10.18653/v1/2020.emnlp-main.135",
    pages = "1728--1736",
}

@software{Skorski_A_working_implmentation_2023,
author = {Skorski, Maciej},
month = {3},
title = {{A working implmentation of the paper Tired of Topic Models? Clusters of Pretrained Word Embeddings...}},
url = {https://github.com/maciejskorski/Cluster-Analysis},
version = {1.0.0},
year = {2023}
}