NLTK
spaCy
NLTK | spaCy | |
---|---|---|
64 | 106 | |
13,035 | 28,751 | |
0.8% | 0.6% | |
8.1 | 9.2 | |
14 days ago | 5 days 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.
spaCy
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Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
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Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
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Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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How to predict this sequence?
spaCy
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What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
- spaCy: Industrial-Strength Natural Language Processing
What are some alternatives?
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
bert - TensorFlow code and pre-trained models for BERT
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
polyglot - Multilingual text (NLP) processing toolkit
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
textacy - NLP, before and after spaCy
Jieba - 结巴中文分词