Python 3 is the future and even though a lot of legacy code and systems run on Python 2 (including our applications, which is why I had written this book in Python 2 in the first place). We need to slowly start migrating and building our code, apps and systems based on Python 3.

Looking for experts in Python 3.x as well as NLP and text analytics who could help out in migrating each chapter's codebase to Python 3.x, since I am occupied for a major part of this year on other projects. I do have some parts of it ready for Python 3.x and can offer help and support whenever needed.

Successful codebase migrations will make sure you are mentioned as a contributor in the acknowledgements & contributor list of this repository and project. Also you will get a mention in future versions of the book whenever that is in the pipeline.

Hello,

the Code from the Book for BM25 is not working for large Datasets.

File "C:\Users\xxx\Anaconda2\lib\site-packages\scipy\sparse\base.py", line 1039, in _process_toarray_args return np.zeros(self.shape, dtype=self.dtype, order=order)

MemoryError

It would be great if someone could change the code that it works in my case. I'm trying this by myself currently, but no success so far :(.

Thanks

Can anyone advise how to fix this urgent problem while using Python 3 ? While I am trying the code in Chapter 4: "----> from normalization import normalize_corpus import nltk from operator import itemgetter

ModuleNotFoundError: No module named 'normalization' "

my nltk is not complete download, because one of module is out of date. So, when i try your code i get in error, on of module is not work.

In [47]: from contractions import CONTRACTION_MAP

ImportError Traceback (most recent call last) in () ----> 1 from contractions import CONTRACTION_MAP

ImportError: No module named contractions

Going through feature_extraction_demo.py, the line:

avg_word_vec_features = averaged_word_vectorizer(corpus=TOKENIZED_CORPUS,
                                                 model=model,
                                                 num_features=10)

raises an AttributionError:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-17-cdd908e72f5c> in <module>()
      2 TOKENIZED_CORPUS
      3 avg_word_vec_features = averaged_word_vectorizer(corpus=TOKENIZED_CORPUS,
----> 4                                                  model=model, num_features=10)

/Users/athair/researchdone/text_analytics_with_python/codes/feature_extractors.pyc in averaged_word_vectorizer(corpus, model, num_features)
     58 
     59 def averaged_word_vectorizer(corpus, model, num_features):
---> 60     vocabulary = set(model.index2word)
     61     features = [average_word_vectors(tokenized_sentence, model, vocabulary, num_features)
     62                     for tokenized_sentence in corpus]

AttributeError: 'Word2Vec' object has no attribute 'index2word'

with the latest gensim: v'3.0.1' I tried with both pip install gensim and from pulling directly from the gensim github repository: https://github.com/RaRe-Technologies/gensim/

The accepted answer here suggests a fix: https://stackoverflow.com/questions/43146077/index2word-in-gensims-doc2vec-raises-an-attribute-error

It would be great for readers to see how to generate annotated dependency tree in Fig 3-4. This is also related to https://stackoverflow.com/a/44867616/2380455, where installation of dependencies has been presented for os x.

When you have a large corpus, first making the matrix dense takes a lot of memory. Doing the dot product first and then expanding the result is more memory-efficient and still gives the same result

Running this line:

normalize our corpus

norm_corpus = tn.normalize_corpus(corpus=data_df['Article'], html_stripping=True, contraction_expansion=True, accented_char_removal=True, text_lower_case=True, text_lemmatization=True, text_stemming=False, special_char_removal=True, remove_digits=True, stopword_removal=True, stopwords=stopword_list)

Returns error: AttributeError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_17860/2616894830.py in 6 7 # normalize our corpus ----> 8 norm_corpus = tn.normalize_corpus(corpus=data_df['Article'], html_stripping=True, contraction_expansion=True, 9 accented_char_removal=True, text_lower_case=True, text_lemmatization=True, 10 text_stemming=False, special_char_removal=True, remove_digits=True,

AttributeError: module 'text_normalizer' has no attribute 'normalize_corpus'

I can't find a reference to normalize_corpus in the text_normalizer documentation. Thanks

Hello Dipanjan,

I was wondering if you had the notebooks in question mentioned in the Safari/OReilly book available? The link led me here and I don't see them in the repo.

Thanks!

This is also on page 356.

from nltk.corpus import sentiwordnet as swn

good = swn.senti_synsets('good', 'n')[0] Traceback (most recent call last): File "", line 1, in TypeError: 'filter' object is not subscriptable

When i run the code in Chapter 6,I got the following error: File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/zipfile.py", line 1267, in _RealGetContents raise BadZipFile("File is not a zip file") zipfile.BadZipFile: File is not a zip file

I tried to use pattern3 but it doesn't work.Google has little about this. I can't solve it. It would be great if someone who have gone through this problem tells me how to solve it. thanks a lot!