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Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics

This is the official repository of the NAACL 2022 paper "Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics".

Paper is available at https://aclanthology.org/2022.naacl-main.285/.

Quick Links

Install Dependencies

conda create -n cluster_topic_model python=3.7 -y
conda activate cluster_topic_model
pip install -r requirements.txt

Run CETopic

Below is an example of how to run CETopic on the BBC dataset. You can choose a word selecting method from [tfidf_idfi, tfidf_tfi, tfidfi, tfi]. If you prefer not to reduce the embedding dimensionalities using UMAP, simply set dim_size=-1. You can train the model, get evaluation results and topics:

from baselines.cetopictm import CETopicTM
from utils import prepare_dataset

dataset, sentences = prepare_dataset('bbc')

tm = CETopicTM(dataset=dataset, 
               topic_model='cetopic', 
               num_topics=5, 
               dim_size=5, 
               word_select_method='tfidf_idfi',
               embedding='princeton-nlp/unsup-simcse-bert-base-uncased', 
               seed=42)

tm.train()
td_score, cv_score, npmi_score = tm.evaluate()
print(f'td: {td_score} npmi: {npmi_score} cv: {cv_score}')

topics = tm.get_topics()
print(f'Topics: {topics}')

You should expect something similar:

td: 0.96 npmi: 0.11889979828579675 cv: 0.7574707739043192

Topics: {
0: [
    ('tory', 0.010655754552013494), 
    ('labour', 0.010140645139665033), 
    ('election', 0.008794514704281466), 
    ('party', 0.007523648919704865), 
    ('government', 0.006801391630922592), 
    ('plan', 0.00444844822680986), 
    ('minister', 0.003928431285391421), 
    ('leader', 0.0037359746494665725), 
    ('pension', 0.003697165535497612), 
    ('lord', 0.0036023621214819595)
  ], ...
}

Run Using Shell

bash run_evaluate.sh

Dataset

We use preproessed dataset from OCTIS. You can choose from [bbc, m10, 20ng].

Topic Model

You can run our model cetopic or you can also choose a baseline model from [lda, prodlda, zeroshottm, combinedtm, bertopic].

Word Selecting Method

If you use cetopic, you can also choose a word selecting method from [tfidf_idfi, tfidf_tfi, tfidfi, tfi].

Pretrained Model

You can choose a pretrained model such as princeton-nlp/unsup-simcse-bert-base-uncased or bert-base-uncased from SimCSE or HuggingFace.

Arguments

usage: main.py [-h] [--topic_model TOPIC_MODEL] [--dataset DATASET] 
[--pretrained_model PRETRAINED_MODEL] [--num_topics NUM_TOPICS] 
[--dim_size DIM_SIZE] [--word_select_method WORD_SELECT_METHOD] [--seed SEED]

Cluster Contextual Embeddings for Topic Models

optional arguments:
  -h, --help            show this help message and exit
  --topic_model TOPIC_MODEL
                        Topic model to run experiments
  --dataset DATASET     Datasets to run experiments
  --pretrained_model PRETRAINED_MODEL
                        Pretrained language model
  --num_topics NUM_TOPICS
                        Topic number
  --dim_size DIM_SIZE   Embedding dimension size to reduce to
  --word_select_method WORD_SELECT_METHOD
                        Word selecting methods to select words from each cluster
  --seed SEED           Random seed

Examples

>> Run cetopic on BBC dataset using tfidf_idfi word selecting method and unsupervised SimCSE embeddings, the embedding dimensionality will be reduced to 5 and will ouput 5 topics:

# run_evaluate.sh

DATASET='bbc'
TOPIC_MODEL='cetopic'
WORD_SELECT_METHOD='tfidf_idfi'
PRETRAINED_MODEL='princeton-nlp/unsup-simcse-bert-base-uncased'

python main.py\
    --topic_model ${TOPIC_MODEL}\
    --dataset ${DATASET}\
    --num_topics 5\
    --dim_size 5\
    --word_select_method ${WORD_SELECT_METHOD}\
    --pretrained_model ${PRETRAINED_MODEL}\
    --seed 30

>> Run bertopic on 20NewsGroup dataset using BERT embeddings and expect to ouput 50 topics. Note that BERTopic may not output the exact specified number of topics:

# run_evaluate.sh

DATASET='20ng'
TOPIC_MODEL='bertopic'
PRETRAINED_MODEL='bert-base-uncased'

python main.py\
    --topic_model ${TOPIC_MODEL}\
    --dataset ${DATASET}\
    --num_topics 50\
    --pretrained_model ${PRETRAINED_MODEL}\

>> Run combinedtm on M10 dataset using RoBERTa embeddings and expect to ouput 75 topics:

# run_evaluate.sh

DATASET='m10'
TOPIC_MODEL='combinedtm'
PRETRAINED_MODEL='roberta-base'

python main.py\
    --topic_model ${TOPIC_MODEL}\
    --dataset ${DATASET}\
    --num_topics 75\
    --pretrained_model ${PRETRAINED_MODEL}\

Add New Models

To add new topic models, you can inherit the base class TopicModel and implement your own train(), evaluate(), and get_topics() functions:

class TopicModel:
    def __init__(self, dataset, topic_model, num_topics):
        self.dataset = dataset
        self.topic_model = topic_model
        self.num_topics = num_topics
        
    def train(self):
        raise NotImplementedError("Train function has not been defined!")

    def evaluate(self):
        raise NotImplementedError("Evaluate function has not been defined!")

    def get_topics(self):
        raise NotImplementedError("Get topics function has not been defined!")

Citation

If our research helps you, please kindly cite our paper:

@inproceedings{zhang-etal-2022-neural,
    title = "Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics",
    author = "Zhang, Zihan  and
      Fang, Meng  and
      Chen, Ling  and
      Namazi Rad, Mohammad Reza",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.285",
    doi = "10.18653/v1/2022.naacl-main.285",
    pages = "3886--3893",
    abstract = "Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, we conduct thorough experiments showing that directly clustering high-quality sentence embeddings with an appropriate word selecting method can generate more coherent and diverse topics than NTMs, achieving also higher efficiency and simplicity.",
}

Acknowledgement

The code is implemented using OCTIS and BERTopic.

License

MIT