Please suggest a good machine learning classifier for truecasing of dataset.
Also, Is it possible to specify out own rules/features for truecasing in such a classifier? Thanks for all your suggestions.
Thanks
I implemented a version of a truecaser in Python. It can be trained for any language when you provide enough data (i.e. correctly cased sentences).
For English, it achieves an accuracy of 98.38% on sample sentences from Wikipedia. A pre-trained model for English is provided.
You can find it here:
https://github.com/nreimers/truecaser
Please take a look at this whitepaper.
http://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf
They report 98% of accuracy.
Related
LayoutLM builds itself on top of BERT as the baseline, but I want to substitute BERT for MobileBERT because BERT is too large. Unfortunately, the Huggingface Transformers library doesn't give you the option to change the baseline model for LayoutLM. How should I go about swapping BERT for MobileBERT? I'm aware they have very different configurations.
I'm aware this is a very broad question and a wide topic, but I can't find anything about it online. How would I go about it and where should I start?
LayoutLM can be traind with the MiniLM models but with a slight accuaracy loss.
Good day, I am a student that is interested in NLP. I have come across the demo on AllenNLP's homepage, which stated that:
The model is a simple LSTM using GloVe embeddings that is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% accuracy on the test set.
Is there any reference to the sample code or any tutorial that I can follow to replicate this result, so that I can learn more about this subject? I am trying to obtain a Regression Output (Instead of classification).
I hope that someone can point me in the right direction.. Any help is much appreciated. Thank you!
AllenAI provides all code for examples and lib opensource on Git, including AllenNLP.
I found exactly how the example was run here: https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/dataset_readers/stanford_sentiment_tree_bank_test.py
However, to make it a Regression task, you'll have to tweak directly on Pytorch, which is the underlying technology for AllenNLP.
i am developing an ASR system for my local kashmiri language and had Already had done some work collected some data and trained a CNN model on it with not so good accuracy but now i want to change the Strategy i want to do this work on phoneme level can any buddy suggest me the way to do it please.
Thanks in Advance.
I am looking for ways to train my GATE. Not just running the application, but training it with like data that already annotated (not just plain document). I really appreciate if anybody help me. Thanks :)
I have been trying to use NER feature of NLTK. I want to extract such entities from the articles. I know that it can not be perfect in doing so but I wonder if there is human intervention in between to manually tag NEs, will it improve?
If yes, is it possible with present model in NLTK to continually train the model. (Semi-Supervised Training)
The plain vanilla NER chunker provided in nltk internally uses maximum entropy chunker trained on the ACE corpus. Hence it is not possible to identify dates or time, unless you train it with your own classifier and data(which is quite a meticulous job).
You could refer this link for performing he same.
Also, there is a module called timex in nltk_contrib which might help you with your needs.
If you are interested to perform the same in Java better look into Stanford SUTime, it is a part of Stanford CoreNLP.