NLTK
rake-nltk
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| NLTK | rake-nltk | |
|---|---|---|
| 64 | 4 | |
| 13,015 | 1,034 | |
| 1.4% | - | |
| 8.1 | 0.0 | |
| 9 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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NLTK
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Building a local AI smart Home Assistant
alternatively, could we not simply split by common characters such as newlines and periods, to split it within sentences? it would be fragile with special handling required for numbers with decimal points and probably various other edge cases, though.
there are also Python libraries meant for natural language parsing[0] that could do that task for us. I even see examples on stack overflow[1] that simply split text into sentences.
[0]: https://www.nltk.org/
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Sorry if this is a dumb question but is the main idea behind LLMs to output text based on user input?
Check out https://www.nltk.org/ and work through it, it'll give you a foundational understanding of how all this works, but very basically it's just a fancy auto-complete.
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Best Portfolio Projects for Data Science
NLTK Documentation
- Where to start learning NLP ?
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Is there a programmatic way to check if two strings are paraphrased?
If this is True, then you need also Natural Language Toolkit to process the words.
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[CROSS-POST] What programming language should I learn for corpus linguistics?
In that case, you should definitely have a look at Python's nltk library which stands for Natural Language Toolkit. They have a rich corpus collection for all kinds of specialized things like grammars, taggers, chunkers, etc.
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Transition to ml, starting with LLM
If not, start with Python's Natural Language Toolkit.
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Learning resources for NLP
Try https://www.nltk.org it runs you through the basics. The book is here
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Which programming language should I learn for NLP and computational linguistics?
In terms of programming languages, Python is a great first programming language. the learnpython subreddit has lots of good recommendations for resources to get started. Once you're comfortable with the language, NLTK would be a good place to start, and the docs have heaps of examples. Check it out https://www.nltk.org/
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Python for stock analysis?
The most popular library to do this is NLTK though I believe you can use some of the popular AI API services today as well. Bloomberg launched one.
rake-nltk
- rake-nltk 1.0.6 released. Comes with the flexibility to choose your own sentence and word tokenizers.
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PMI for WordClouds
I'm not sure what you mean by tokenizing phrases or concepts. Specifically extracting institution names would fall under NER. You can do this with spaCy. Extracting commonly used phrases would fall under keyword extraction. For this, you can study frequencies of n-grams of length > 1 and optionally filter based on POS (i.e. NOUN+ADJ). I've never used RAKE (https://github.com/csurfer/rake-nltk) but I've heard this is also a popular method.
What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
yake - Single-document unsupervised keyword extraction
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
pke - Python Keyphrase Extraction module
bert - TensorFlow code and pre-trained models for BERT
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
WordDumb - A calibre plugin that generates Kindle Word Wise and X-Ray files for KFX, AZW3, MOBI and EPUB eBook.
polyglot - Multilingual text (NLP) processing toolkit
hepscrape - arXiv:hep-ph scraper
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.