NLTK | lxml | |
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64 | 17 | |
13,035 | 2,573 | |
0.8% | 0.7% | |
8.1 | 9.6 | |
13 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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.
lxml
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8 Most Popular Python HTML Web Scraping Packages with Benchmarks
lxml
- Looking for someone to web scrape housing data needed research. Will pay you for your work!!
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13 ways to scrape any public data from any website
Parsel is a library build to extract data from XML/HTML documents with XPath and CSS selectors support, and could be combined with regular expressions. It's usees lxml parser under the hood by default.
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lazy and fast .mpd file parser - for video streaming
So, now that I no longer work in that industry, and I had some free time, I created a lazy parsing package using lxml instead of the xml parser in the standard library, which can help people who want to have a python only parsing solution.
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Guide to working with fancier XML documents with python?
Seriously, use LXML.
- There is framework for everything.
- how to find text in website ?
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Parsing XML file deletes whitespace. How to avoid it?
I got curious about this now so I did some tests on my own, and it appears that the XML parser implementation in Python does indeed strip all newline characters from attributes. Whether this is according to XML standard I do not know; I also briefly tried an alternative XML implementation for Python and it behaves the same, so I would assume that this is standard behavior, but I'm not knowledgable enough about XML to say for certain.
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Use case for ETL over ELT?
I use lxml for the XML parsing and pyodbc as the ODBC library. We have a small team so I just keep it as simple as possible: 1. A cursor yields the XML documents from a SQL query as a stream 2. A generator function parses the XML document and yields the rows (you could parallelize this step) 3. Stream each of the resulting rows to a single CSV file 4. Scoop up the resulting CSV file into the target database (usually with the DB engine's loader; bulk insert isn't so fast over ODBC) It ends up being a straight forward, low-overhead approach.
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CompactLogix: Implementing HTTP requests & XML Data Transfer via TCP/IP
If that sounds too weird maybe take a look at pycomm3, python also has lxml as well as requests. You could write a script that retrieves the data from the clx using the appropriate pycomm3 driver for cplx and then do xml things with the data using lxml and transmit the data over http using requests.
What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
xmltodict - Python module that makes working with XML feel like you are working with JSON
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
selectolax - Python binding to Modest and Lexbor engines (fast HTML5 parser with CSS selectors).
bert - TensorFlow code and pre-trained models for BERT
html5lib - Standards-compliant library for parsing and serializing HTML documents and fragments in Python
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
untangle - Converts XML to Python objects
polyglot - Multilingual text (NLP) processing toolkit
bleach - Bleach is an allowed-list-based HTML sanitizing library that escapes or strips markup and attributes
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
pyquery - A jquery-like library for python