A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

Last update: Oct 07, 2022

Overview

Multilingual Latent Dirichlet Allocation (LDA) Pipeline

This project is for text clustering using the Latent Dirichlet Allocation (LDA) algorithm. It can be adapted to many languages provided that the Snowball stemmer, a dependency of this project, supports it.

Usage

from artifici_lda.lda_service import train_lda_pipeline_default


FR_STOPWORDS = [
    "le", "les", "la", "un", "de", "en",
    "a", "b", "c", "s",
    "est", "sur", "tres", "donc", "sont",
    # even slang/texto stop words:
    "ya", "pis", "yer"]
# Note: this list of stop words is poor and is just as an example.

fr_comments = [
    "Un super-chat marche sur le trottoir",
    "Les super-chats aiment ronronner",
    "Les chats sont ronrons",
    "Un super-chien aboie",
    "Deux super-chiens",
    "Combien de chiens sont en train d'aboyer?"
]

transformed_comments, top_comments, _1_grams, _2_grams = train_lda_pipeline_default(
    fr_comments,
    n_topics=2,
    stopwords=FR_STOPWORDS,
    language='french')

print(transformed_comments)
print(top_comments)
print(_1_grams)
print(_2_grams)

Output:

array([[0.14218195, 0.85781805],
       [0.11032992, 0.88967008],
       [0.16960695, 0.83039305],
       [0.88967041, 0.11032959],
       [0.8578187 , 0.1421813 ],
       [0.83039303, 0.16960697]])

['Un super-chien aboie', 'Les super-chats aiment ronronner']

[[('chiens', 3.4911404011996545), ('super', 2.5000203653313933)],
 [('chats',  3.4911393765493255), ('super', 2.499979634668601 )]]

[[('super chiens', 2.4921035508342464)],
 [('super chats',  2.492102155345991 )]]

How it works

See Multilingual-LDA-Pipeline-Tutorial for an exhaustive example (intended to be read from top to bottom, not skimmed through). For more explanations on the Inverse Lemmatization, see Stemming-words-from-multiple-languages.

Supported Languages

Those languages are supported:

Danish
Dutch
English
Finnish
French
German
Hungarian
Italian
Norwegian
Porter
Portuguese
Romanian
Russian
Spanish
Swedish
Turkish

You need to bring your own list of stop words. That could be achieved by computing the Term Frequencies on your corpus (or on a bigger corpus of the same language) and to use some of the most common words as stop words.

Dependencies and their license

numpy==1.14.3           # BSD-3-Clause and BSD-2-Clause BSD-like and Zlib
scikit-learn==0.19.1    # BSD-3-Clause
PyStemmer==1.3.0        # BSD-3-Clause and MIT
snowballstemmer==1.2.1  # BSD-3-Clause and BSD-2-Clause
translitcodec==0.4.0    # MIT License
scipy==1.1.0            # BSD-3-Clause and MIT-like

Unit tests

Run pytest with ./run_tests.sh. Coverage:

----------- coverage: platform linux, python 3.6.7-final-0 -----------
Name                                       Stmts   Miss  Cover
--------------------------------------------------------------
artifici_lda/__init__.py                       0      0   100%
artifici_lda/data_utils.py                    39      0   100%
artifici_lda/lda_service.py                   31      0   100%
artifici_lda/logic/__init__.py                 0      0   100%
artifici_lda/logic/count_vectorizer.py         9      0   100%
artifici_lda/logic/lda.py                     23      7    70%
artifici_lda/logic/letter_splitter.py         36      4    89%
artifici_lda/logic/stemmer.py                 60      3    95%
artifici_lda/logic/stop_words_remover.py      61      5    92%
--------------------------------------------------------------
TOTAL                                        259     19    93%

License

This project is published under the MIT License (MIT).

Coded by Guillaume Chevalier at Neuraxio Inc.

A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

Related tags

Overview

Multilingual Latent Dirichlet Allocation (LDA) Pipeline

Usage

How it works

Supported Languages

Dependencies and their license

Unit tests

License

Owner

Artifici Online Services inc.

A collection of models for image - text generation in ACM MM 2021.

Reading Wikipedia to Answer Open-Domain Questions

News-Articles-and-Essays - NLP (Topic Modeling and Clustering)

STonKGs is a Sophisticated Transformer that can be jointly trained on biomedical text and knowledge graphs

NLP: SLU tagging

Incorporating KenLM language model with HuggingFace implementation of Wav2Vec2CTC Model using beam search decoding

Comprehensive-E2E-TTS - PyTorch Implementation

What are the best Systems? New Perspectives on NLP Benchmarking

Easy, fast, effective, and automatic g-code compression!

Simplified diarization pipeline using some pretrained models - audio file to diarized segments in a few lines of code

Non-Autoregressive Predictive Coding

Pytorch-Named-Entity-Recognition-with-BERT

Python library for interactive topic model visualization. Port of the R LDAvis package.

RuCLIP-SB (Russian Contrastive Language–Image Pretraining SWIN-BERT) is a multimodal model for obtaining images and text similarities and rearranging captions and pictures. Unlike other versions of the model we use BERT for text encoder and SWIN transformer for image encoder.

An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

초성 해석기 based on ko-BART

The ability of computer software to identify words and phrases in spoken language and convert them to human-readable text

NeurIPS'21: Probabilistic Margins for Instance Reweighting in Adversarial Training (Pytorch implementation).

Neural network sequence labeling model