Nltk Download UPD Directory

A new window should open, showing the NLTK Downloader. Click on the File menu and select Change Download Directory. For central installation, set this to C:\nltk_data (Windows), /usr/local/share/nltk_data (Mac), or /usr/share/nltk_data (Unix). Next, select the packages or collections you want to download.

Now at the university I only have access to /home/XX/bin and not /home/XX directly. So is there anyway I could paste the wordnet corpus into /home/XX/bin and then somehow make nltk look for the corpus in that folder?

Nltk Download Directory

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That's when I enter a filename that I've uploaded myself. If I enter "text1" as the filename, as I would in the python shell version after downloading the sample files, I get "No such file or directory: 'text1'." In this case, I understand the error, in that I know I haven't gotten the text1 file ready for the program to use, but I don't know how to solve the problem.

I am testing out the plugin 'Text Summarizations' and successfully built the code environment in a machine with no internet access . However, I am getting the following NLTK tokenizers error: I have actually manually upload the nltk resources using the Resources tab as shown below, however it still shows the same error. Can anyone advise?

We would not want these words to take up space in our database, or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to stop words. NLTK(Natural Language Toolkit) in python has a list of stopwords stored in 16 different languages. You can find them in the nltk_data directory. home/pratima/nltk_data/corpora/stopwords are the directory address.(Do not forget to change your home directory name)

The simplest thing to do is read our plaintext corpus, as we have to write no code to do so. Instead we can simply use the nltk.corpus.PlaintextCorpusReader directly, instantiating it with the correct path and pattern for our files. For the DDL corpus this looks like something as follows:

Hey Dan,

Very Nice Guide. Thanks for it.

But I am having issue with nltk library as heroku not able to download it in server but it working fine with local machine.

Here is the error which it gives.

Assuming you have nltk specified in the requirements.txt file aswell? I think this tells Heroku to then open the nltk.txt file itself. Assume you do because it says downloading NLTK and then looks for the text file.

This is happening because we are not allowing outbound call from AI Fabric and this is what nltk is trying to do (downloading data from outside). In order to solve that you need to incorporate nltk data in ML Package that you are uploading.

Note on IDLE: When I lauch IDLE, it sets the default working directory to my Documents folderrather than to my home folder, so the simplest way to work this out if you are using IDLE insteadof Spyder is to put the files you want Python to find in the Documents folder.

If you wish to share the downloaded packages with many system users, you can choose a custom location accessible to every user running nltk. Some locations would make it available without extra effort, and they are all listed in the nltk.data.path Python property.

I am experiencing problems with import < module >. I just installed colorama and import colorama works fine. import tabulate, import numpy, form prettytable import PrettyTable, (including import prettytable / import PrettyTable) all raise ModuleNotFoundError. Yet all four were installed to the directory provided in original post.

which refers to the path C:\Users\atomi\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\Scripts

which does not look entirely like a normal install path. In particular,

the PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0 looks like

scratch/temporary directory name. That may also speak to why the

Scripts folder is not in PATH.

The Stanford PoS Tagger is itself written in Java, so can be easily integrated in and called from Java programs. However, many linguists will rather want to stick with Python as their preferred programming language, especially when they are using other Python packages such as NLTK as part of their workflow. And while the Stanford PoS Tagger is not written in Python, it can nevertheless be more or less seamlessly integrated into Python programs. In this tutorial, we will be looking at two principal ways of driving the Stanford PoS Tagger from Python and show how this can be done with single files and with multiple files in a directory.

# running the Stanford POS Tagger from NLTKimport nltkfrom nltk import word_tokenizefrom nltk import StanfordTaggertext_tok = nltk.word_tokenize("Just a small snippet of text.")# print(text_tok)pos_tagged = nltk.pos_tag(text_tok)# print the list of tuples: (word,word_class)print(pos_tagged)# for loop to extract the elements of the tuples in the pos_tagged list# print the word and the pos_tag with the underscore as a delimiterfor word,word_class in pos_tagged: print(word + "_" + word_class)

# running the Stanford POS Tagger from NLTKimport nltkfrom nltk import word_tokenizefrom nltk import StanfordTagger# point this path to a utf-8 encoded plain text file in your own file systemf = "C:/Users/Public/projects/python101-2018/data/sample-text.txt"text_raw = open(f).read()text = nltk.word_tokenize(text_raw)pos_tagged = nltk.pos_tag(text)# print the list of tuples: (word,word_class)# this is just a test, comment out if you do not want this outputprint(pos_tagged)# for loop to extract the elements of the tuples in the pos_tagged list# print the word and the pos_tag with the underscore as a delimiterfor word,word_class in pos_tagged: print(word + "_" + word_class)

# Stanford POS tagger - Python workflow for using a locally installed version of the Stanford POS Tagger# Python version 3.7.1 | Stanford POS Tagger stand-alone version 2018-10-16import nltkfrom nltk import *from nltk.tag.stanford import StanfordPOSTaggerfrom nltk.tokenize import word_tokenize# enter the path to your local Java JDK, under Windows, the path should look very similar to this examplejava_path = "C:/Program Files/Java/jdk1.8.0_192/bin/java.exe"os.environ["JAVAHOME"] = java_path# enter the paths to the Stanford POS Tagger .jar file as well as to the model to be usedjar = "C:/Users/Public/utility/stanford-postagger-full-2018-10-16/stanford-postagger.jar"model = "C:/Users/Public/utility/stanford-postagger-full-2018-10-16/models/english-bidirectional-distsim.tagger"pos_tagger = StanfordPOSTagger(model, jar, encoding = "utf-8")# Tagging this one example sentence as a test:# this small snippet of text lets you test whether the tagger is running before you attempt to run it on a locally# stored file (see line 28)text = "Just a small snippet of text to test the tagger."# Tagging a locally stored plain text file:# as soon as the example in line 22 is running ok, comment out that line (#) and comment in the next line and# enter a path to a local file of your choice;# the assumption made here is that the file is a plain text file with utf-8 encoding# text = open("C:/Users/Public/projects/python101-2018/data/sample-text.txt").read()# nltk word_tokenize() is used here to tokenize the text and assign it to a variable 'words'words = nltk.word_tokenize(text)# print(words)# the pos_tagger is called here with the parameter 'words' so that the value of the variable 'words' is assigned pos tagstagged_words = pos_tagger.tag(words)print(tagged_words)

# Stanford POS tagger - Python workflow for using a locally installed version of the Stanford POS Tagger# Python version 3.7.1 | Stanford POS Tagger stand-alone version 2018-10-16import nltkfrom nltk import *from nltk.tag.stanford import StanfordPOSTaggerfrom nltk.tokenize import word_tokenize# enter the path to your local Java JDK, under Windows, the path should look very similar to this examplejava_path = "C:/Program Files/Java/jdk1.8.0_192/bin/java.exe"os.environ["JAVAHOME"] = java_path# enter the paths to the Stanford POS Tagger .jar file as well as to the model to be usedjar = "C:/Users/Public/utility/stanford-postagger-full-2018-10-16/stanford-postagger.jar"model = "C:/Users/Public/utility/stanford-postagger-full-2018-10-16/models/english-bidirectional-distsim.tagger"pos_tagger = StanfordPOSTagger(model, jar, encoding = "utf-8")# Tagging this one example sentence as a test:# this small snippet of text lets you test whether the tagger is running before you attempt to run it on a locally# stored file (see line 28)# text = "Just a small snippet of text to test the tagger."# Tagging a locally stored plain text file:# as soon as the example in line 22 is running ok, comment out that line (#) and comment in the next line and# enter a path to a local file of your choice;# the assumption made here is that the file is a plain text file with utf-8 encodingtext = open("C:/Users/Public/projects/python101-2018/data/sample-text.txt").read()# nltk word_tokenize() is used here to tokenize the text and assign it to a variable 'words'words = nltk.word_tokenize(text)# print(words)# the pos_tagger is called here with the parameter 'words' so that the value of the variable 'words' is assigned pos tagstagged_words = pos_tagger.tag(words)print(tagged_words)

Please note down the name of the directory to which you have unpacked the Stanford PoS Tagger as well as the subdirectory in which the tagging models are located. Also write down (or copy) the name of the directory in which the file(s) you would like to part of speech tag is located.As we will be writing output of the two subprocesses of tokenization and tagging to files in your file system, you have to create these output directories in your file system and again write down or copy the locations to your clipboard for further use. In this example these directories are called:

Depending on your installation, your nltk_data directory might be hiding in a multitude of locations. To figure out where it is, head to your Python directory, where the NLTK module is. If you do not know where that is, use the following code: e24fc04721