CoreNLP
spaCy
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CoreNLP | spaCy | |
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11 | 106 | |
9,451 | 28,660 | |
0.9% | 1.2% | |
9.1 | 9.3 | |
5 days ago | 7 days ago | |
Java | Python | |
GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
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?
- 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?
Apache OpenNLP - Apache OpenNLP
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.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
NLTK - NLTK Source
DKPro Core - Collection of software components for natural language processing (NLP) based on the Apache UIMA framework.
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
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.
polyglot - Multilingual text (NLP) processing toolkit
Apache Solr - Apache Lucene and Solr open-source search software
textacy - NLP, before and after spaCy