Having the Apache Math commons with the NN is great, however, I could not find the simplest usage examples. Any simple examples to experiment with the Apache math NN ?
Answer: https://github.com/apache/commons-math/tree/master/src/test/java/org/apache/commons/math4/ml/neuralnet
cheers !
Related
I have a normal classifier which I made from the nltk twittes, more on that in this article: https://www.digitalocean.com/community/tutorials/how-to-perform-sentiment-analysis-in-python-3-using-the-natural-language-toolkit-nltk
I want to get the positive/negative percentage of a sentence. How do I do that?
You can use TextBlob library for sentiment polarity. If you'd like, you'll investigate these sites:
Planspace.org
Stackabuse.com
Towarddatascience.com
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.
Reading https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala this implementation of Word2Vec is a port of Google Word2Vec https://code.google.com/archive/p/word2vec/
Is this an implementation of paper 'Efficient Estimation of Word Representations in Vector Space' : https://arxiv.org/abs/1301.3781 ?
Tensorflow Word2Vec does reference paper 'Efficient Estimation of Word Representations in Vector Space' .
What then is difference between implementations of Apache Spark and Tensorflow Word2Vec and under what conditions should each be used ?
There are different ways to implement word2vec, but according to pyspark they do it by skip grams ( https://spark.apache.org/docs/2.1.0/mllib-feature-extraction.html#word2vec ). Tensorflow docs say they also use the skip gram model (https://www.tensorflow.org/tutorials/word2vec). From just glancing at the two docs, its seems like they calculate them the same way as well.
Spark does really well on a distributed environment, and from what I am not aware of the benchmarks of tensorflow vs mllib as data gets bigger exactly, as Tensorflow distributed is fairly new.
Could anyone give me an Example in detail which show how SVM exactly work with all the necessary Mathematics?
As I tried to search in the internet and found very little example about SVM.
Thank you.
You can watch the stanford lectures on machine learning from lectures 6 to 8. Its on youtube. It teaches you everything on SVM and the mathematics involved in SVM. Have a look at it..
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.