NCRFpp
Stanza
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| NCRFpp | Stanza | |
|---|---|---|
| 1 | 8 | |
| 1,877 | 7,043 | |
| - | 1.0% | |
| 0.0 | 9.7 | |
| almost 2 years ago | 1 day ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
NCRFpp
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Speech and Language Processing (3rd ed. draft)
They still talk about Hidden Markov Models (HMMs) in quite a bit of detail in the sequence labelling chapter, but you are quite right, Conditional Random Fields (CRFs) and especially neural network based CRFs are in the top rankings when it comes to named entity recognition (NER) and part-of-speech tagging (POS), e.g. see https://github.com/jiesutd/NCRFpp.
Stanza
- Down and Out in the Magic Kingdom
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Parts of speech tagged for German
I use Python's spacy library: https://spacy.io/models/de or stanza: https://stanfordnlp.github.io/stanza/ each with their respective language models.
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Off the shelf sentence parsers?
stanza has a constituency parser. There's a model compatible with the dev branch with an accuracy of 95.8 on PTB, using Roberta as a bottom layer, so it's pretty decent at this point. (The currently released model is not as accurate, but it's easy to get the better model to you.) There's also Tregex as a Java addon which can very easily search for a noun phrase highest up in the tree: NP !>> NP will search for a noun phrase which is not dominated by any higher up noun phrase.
- The Spacy NER model for Spanish is terrible
- Spacy vs NLTK for Spanish Language Statistical Tasks
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Stanza not tokenising sentences as expected
I am using Stanza to tokenise the sentences:
- Stanza – A Python NLP Package for Many Human Languages
What are some alternatives?
zshot - Zero and Few shot named entity & relationships recognition
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
NLTK - NLTK Source
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
SimpNet-Deep-Learning-in-a-Shader - A trainable convolutional neural network inside a fragment shader
Jieba - 结巴中文分词
nn-morse - Decode morse using a neural network
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
pytorch-partial-crf - CRF, Partial CRF and Marginal CRF in PyTorch
pytext - A natural language modeling framework based on PyTorch