PolyFuzz
contextualized-topic-models
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| PolyFuzz | contextualized-topic-models | |
|---|---|---|
| 2 | 7 | |
| 716 | 1,157 | |
| - | 1.2% | |
| 3.8 | 5.0 | |
| 5 days ago | 3 months ago | |
| Python | Python | |
| MIT License | MIT License |
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PolyFuzz
contextualized-topic-models
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[Project]Topic modelling of tweets from the same user
In our experiments, CTM works well with tweets: https://github.com/MilaNLProc/contextualized-topic-models (I'm one of the authors)
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Extract words from large data set of reviews by sentiment
Use CTM https://github.com/MilaNLProc/contextualized-topic-models with sentiment labels to built distribution of words over labels
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Using Transformer for Topic Modeling - what are the options?
This library from MILA seems quite neat! I haven’t had the change to play with it though : https://github.com/MilaNLProc/contextualized-topic-models
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Catogorize the Data- Topic Modelling algorithm
a bit of shameless self-promotion, but we developed a topic model (https://github.com/MilaNLProc/contextualized-topic-models) that actually supports that use case!
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(NLP) Best practices for topic modeling and generating interesting topics?
If you use CTM, you can provide the topic model two inputs: the preprocessed texts (that will be used by the topic model to generate the topical words) and the unpreprocessed texts (to generate the contextualized representations that will be later concatenated to the document bag-of-word representation). We saw that this slightly improves the performance instead of providing BERT the already-preprocessed text. This feature is supported in the original implementation of CTM, not in OCTIS. See here: https://github.com/MilaNLProc/contextualized-topic-models#combined-topic-model
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Latest trends in topic modelling?
Cross-lingual Contextualized Topic Models with Zero-shot Learning from a team at MilaNLP which uses bag of words representations in combination with multi lingual embeddings from SBERT and works like a VAE (encode the input, use the encoded representation to decode back to a bag of words as close to the input as possible). Using SBERT embeddings makes their model generalise for other languages which may be useful. One major shortfall of this model as I understand is that it can't deal with long documents very elegantly - only up to BERT'S word limit (the workaround is to truncate and use the first words)
What are some alternatives?
RapidFuzz - Rapid fuzzy string matching in Python using various string metrics
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
go-edlib - 📚 String comparison and edit distance algorithms library, featuring : Levenshtein, LCS, Hamming, Damerau levenshtein (OSA and Adjacent transpositions algorithms), Jaro-Winkler, Cosine, etc...
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
simplematch - Minimal, super readable string pattern matching for python.
tika-python - Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
string_grouper - Super Fast String Matching in Python
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
stringlifier - Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
recommendation-system - Build a Content-Based Movie Recommender System (TF-IDF, BM25, BERT)
Sentimentanalysis - Language independent sentiment analysis