PLOD-AbbreviationDetection
converse
PLOD-AbbreviationDetection | converse | |
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1 | 6 | |
9 | 176 | |
- | 0.0% | |
0.0 | 0.0 | |
over 1 year ago | 11 months ago | |
Jupyter Notebook | Jupyter Notebook | |
Creative Commons Attribution Share Alike 4.0 | Apache License 2.0 |
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PLOD-AbbreviationDetection
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Clustering to find abbreviations
Finally, the main problem with unsupervised learning is that you won't be able to reliably measure system performance or improvement. In my view, any time you can spend annotating and collecting data for a (semi-)supervised solution will be well-spent. Existing datasets can also get you started with model development, such as https://github.com/surrey-nlp/PLOD-AbbreviationDetection. Once you have a good model on a conventional dataset, you should be able to start generalizing it to your specific task/dataset.
converse
- Pyconverse – Conversational transcript text analysis library
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[P] Pyconverse - Conversational Text transcript analysis library
Github project link: pyconverse
- GitHub Project Showcase: Conversational Transcript Analysis using various NLP techniques
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PyConverse: Conversational Text Transcript Analysis library.
Github project link: https://github.com/AnjanaRita/converse
- Conversational Text analysis library in Python: PyConverse
What are some alternatives?
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TopMost - A Topic Modeling System Toolkit
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
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nlphose - Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !