CoreNLP
NLTK
CoreNLP | NLTK | |
---|---|---|
11 | 64 | |
9,469 | 13,054 | |
0.5% | 1.0% | |
9.1 | 8.1 | |
7 days ago | 19 days ago | |
Java | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
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CoreNLP
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How does "Reclaim.ai" use AI for smart rescheduling?
The Stanford CoreNLP Model
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One does not simply "create a visualization" from unstructured data!
If your looking at spacy have a look at Apache OpenNLP and Core NLP.
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Has anyone here ever used the seaNMF model for short text topic modeling, and be willing to help me get started with it?
Tokenize with NLTK, SpaCy or CoreNLP
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How to use CoreNLP with a large corpus(14.7 GB)?
It should not take nearly that long. However, again I must recommend you take this conversation to github
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What universities are hubs for reinforcement learning research?
Stanford has a great program and the Stanford NLP Group maintains CoreNLP which I have used before.
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POS-Tagger for declension of German words in Java?
So why not use the Stanford CoreNLP library?
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A comparison of libraries for named entity recognition
If you need NER, there’s no need to implement it yourself. There are several popular libraries that can do this for you nowadays. Five of these libraries, Stanford CoreNLP, NLTK, OpenNLP, SpaCy, and GATE, were already mentioned in the title.
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Making my own AI assistant
Check something like this out to start: https://stanfordnlp.github.io/CoreNLP/
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Good tutorials for PyTorch?
You don't actually even need to learn how to do deep learning if you're doing something fairly basic, which it sounds like you are. There are a lot of good tools you can use basically straight out of the box for something like this. Check out https://huggingface.co/course/chapter1, https://course.spacy.io/en/, https://guide.allennlp.org/ and https://www.nltk.org/book/. If java's more your thing, add https://stanfordnlp.github.io/CoreNLP/ to the list.
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[D] Java vs Python for Machine learning
To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.
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.
What are some alternatives?
Apache OpenNLP - Apache OpenNLP
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
Mallet - MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
bert - TensorFlow code and pre-trained models for BERT
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
DKPro Core - Collection of software components for natural language processing (NLP) based on the Apache UIMA framework.
polyglot - Multilingual text (NLP) processing toolkit
CogCompNLP - CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)