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Please share a link to a dataset that is effective for practicing NLP (Natural Language Processing).
I am beginner level and would like to improve my skills.
NLP is a very broad field, so if you want a specific dataset, you need to name a specific NLP problem (such as NER, sentiment analysis, summarization, etc.), and probably a specific language in which you want to solve this problem.
But still, there are places to look for NLP datasets and problems in general:
the NLP tag on Kaggle: https://www.kaggle.com/tags/nlp
a list of NLP datasets: https://github.com/niderhoff/nlp-datasets
a similar question on DS StackExchange: https://datascience.stackexchange.com/questions/6798/list-of-nlp-challenges
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I'm new to machine learning and I'm trying to create a machine learning classifier using python & Jupyter to match textual advertisements to websites that they will be displayed on. Is there any dataset that I could make use of?
You could get a lot of datas to train your NN e.g. on the U.S. government site
Or you load datasets from sklearn: e.g.:
from sklearn import datasets (link to sklearn)
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I am wondering is there any polish language or Slavic language model on the base of which I could build a new model with my training set?
There're a lot of pretrained embedders, like LASER from Facebook. There's unofficial pypi lib, but it works just fine. If you want to reach seminal-like scores, there's no point in doing this all by hand. Embedders usually covers dozens of languages, so you can feed training data in any language you want. Your models will also work for those language out-of-the-box, even if you trained them on other languages.
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I am looking for a python package that supports Monte Carlo Cross Validation (Repeated random sub-sampling validation). SkLearn has k-fold, but this will not allow me to specify the ratio of training/testing.
I have seen a package in R that will supposedly achieve this (Caret), but is there an equivalent for python?
The package you're after is in fact available in Scikit learn, but is called ShuffleSplit.
Check also the user guide here, where the function is referred to as Random permutations cross-validation.
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Often on the Internet I saw such images and now I am interested in the implementation of this algorithm. The input is a template and a set of words, on the basis of which the image is drawn, the more often the word is used the more space it takes. As far as I know there is already a library for python that allows you to generate such images. Could you tell me which one?
Word cloud allows you to do this, and using its masks features should allow you to shape them: https://github.com/amueller/word_cloud would give you more information on how this can be done.
The above image is constructed using wordCloud!. Here you can find a tutorial on python.
I hope this helps...
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Given text documents (student essays with about 100 words per essay) I want to extract entities and relationships important to the context of the sentence (maybe by considering Noun Phrase and Verb Phrase) to automatically score the answer.
Are there any popular algorithms/tools that I can use to perform this task?
It would be helpful if you could be more specific, but in general this problem is known as Information Extraction. One example software package that deals with it is Standford NLP's open information extraction system. Example use: