tika-python
contextualized-topic-models
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tika-python | contextualized-topic-models | |
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4 | 7 | |
1,411 | 1,157 | |
- | 1.2% | |
2.2 | 5.0 | |
12 days ago | 3 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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tika-python
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Document Parsing - an unsolved problem?
At my previous job we had the same problem which we solved by using Tika. We called it on the server along with other stuff, but there is also a Python binding.
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Extract text from PDF
Tika is from Apache so yes its original code base is Java but it has bindings in other languages. Checkout Tika-Python!
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Extract text from documents
The Textractor instance is the main entrypoint for extracting text. This method is backed by Apache Tika, a robust text extraction library written in Java. Apache Tika has support for a large number of file formats: PDF, Word, Excel, HTML and others. The Python Tika package automatically installs Tika and starts a local REST API instance used to read extracted data.
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?
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
layout-parser - A Unified Toolkit for Deep Learning Based Document Image Analysis
PolyFuzz - Fuzzy string matching, grouping, and evaluation.
py-pdf-parser - A Python tool to help extracting information from structured PDFs.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
paperetl - 📄 ⚙️ ETL processes for medical and scientific papers
Sentimentanalysis - Language independent sentiment analysis