CogCompNLP
CoreNLP
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| CogCompNLP | CoreNLP | |
|---|---|---|
| - | 11 | |
| 469 | 9,451 | |
| 0.0% | 0.9% | |
| 0.0 | 9.1 | |
| 10 months ago | 5 days ago | |
| Java | Java | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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CogCompNLP
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Tracking mentions began in Dec 2020.
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.
What are some alternatives?
Apache OpenNLP - Apache OpenNLP
DKPro Core - Collection of software components for natural language processing (NLP) based on the Apache UIMA framework.
spaCy - đź’« Industrial-strength Natural Language Processing (NLP) in Python
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.
simplenlg - Java API for Natural Language Generation. Originally developed by Ehud Reiter at the University of Aberdeen’s Department of Computing Science and co-founder of Arria NLG. This git repo is the official SimpleNLG version.
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
OpenNRE - An Open-Source Package for Neural Relation Extraction (NRE)
Apache Solr - Apache Lucene and Solr open-source search software
java - Java bindings for TensorFlow