nltk | NLTK the Natural Language Toolkit | Natural Language Processing library

 by   nltk Python Version: 3.8.1 License: Apache-2.0

kandi X-RAY | nltk Summary

kandi X-RAY | nltk Summary

nltk is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. nltk has no bugs, it has build file available, it has a Permissive License and it has medium support. However nltk has 4 vulnerabilities. You can install using 'pip install nltk' or download it from GitHub, PyPI.

NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. NLTK requires Python version 3.7, 3.8, 3.9 or 3.10. For documentation, please visit

            kandi-support Support

              nltk has a medium active ecosystem.
              It has 12020 star(s) with 2746 fork(s). There are 469 watchers for this library.
              It had no major release in the last 12 months.
              There are 231 open issues and 1480 have been closed. On average issues are closed in 145 days. There are 16 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of nltk is 3.8.1

            kandi-Quality Quality

              nltk has no bugs reported.

            kandi-Security Security

              nltk has 4 vulnerability issues reported (0 critical, 4 high, 0 medium, 0 low).

            kandi-License License

              nltk is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              nltk releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed nltk and discovered the below as its top functions. This is intended to give you an instant insight into nltk implemented functionality, and help decide if they suit your requirements.
            • Train the model .
            • Process relation relations .
            • Generate node coordinates for node .
            • Perform a postag regression on the model .
            • Create a LU for the given function .
            • returns a list of words
            • Compute the BLEU score .
            • Train a hidden Markov model .
            • Example demo .
            • Find a jar file for the given name pattern .
            Get all kandi verified functions for this library.

            nltk Key Features

            No Key Features are available at this moment for nltk.

            nltk Examples and Code Snippets

            For loop writing rows into variables
            Pythondot img1Lines of Code : 108dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            with open("somefile.txt") as infile:
                data = # this seems to work OS agnostic
            item = {
                "title": data[0][4:],
                "contents": [{"tag": line.split("##")[0], "sentence": line.split("##")[1]} for line in data
            Identify strings having words from two different lists
            Pythondot img2Lines of Code : 7dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            s_lists = [set(list1), set(list2)]
            df['Result'] = [all(s_lst.intersection(s.split()) for s_lst in s_lists) for s in df['string'].tolist()]
               index                                       string  Result
            0      1  The
            Identify strings having words from two different lists
            Pythondot img3Lines of Code : 14dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df['Result'] = (df['string'].str.contains('|'.join(list1)) 
             & df['string'].str.contains('|'.join(list2)))
                                                    string  Result
            0  The quick brown fox jumps over the lazy dog  
            Get first element of tokenized words in a row
            Pythondot img4Lines of Code : 3dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df["first_name"] = df['Name'].map(lambda x: x.split(' ')[0])
            df["last_name"] = df['Name'].map(lambda x: x.split(' ')[1])
            Pandas - Keyword count by Category
            Pythondot img5Lines of Code : 24dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df["Text"] = (
                .replace([r'\|', RE_stopwords], [' ', ''], regex=True)
                #' ')
                .str.split()  # Previously .split()
              Category          Text
            How to go through each row with pandas apply() and lambda to clean sentence tokens?
            Pythondot img6Lines of Code : 24dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            dataset['gid'] = range(1, dataset.shape[0] + 1)
                   tokenized_sents  gid
            0  [This, is, a, test]    1
            1    [and, this, too!]    2
            clean_df = dataset.explode('tokenized_sents')
              tokenized_sents  gid
            Unable to instantiate a python class - AttributeError: class object has no attribute 'language'
            Pythondot img7Lines of Code : 25dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import nltk
            import pandas as pd
            from nltk.stem.wordnet import WordNetLemmatizer
            from sklearn.base import BaseEstimator, TransformerMixin
            class TextNormalizer(BaseEstimator, TransformerMixin
            How to use Stemming algorithm for a list of words in python
            Pythondot img8Lines of Code : 38dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # download wordnet
            import nltk
            # import these modules
            from nltk.stem import WordNetLemmatizer
            from nltk.corpus import wordnet 
            lemmatizer = WordNetLemmatizer()
            # choose some words to be 
            how to remove specific word from an array that is starts with "[ "?
            Pythondot img9Lines of Code : 7dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            sentence = "[42] On 20 January 1987, he also turned out as substitute for Imran Khan's side in an exhibition game at Brabourne Stadium in Bombay, to mark the golden jubilee of Cricket Club of India."
            words = sentence.split()
            words = [ w fo
            How to properly include data folder to python package
            Pythondot img10Lines of Code : 2dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            package_data={'my_pkg' :['my_pkg/resources/nltk_data/*']}

            Community Discussions


            Pandas - Keyword count by Category
            Asked 2022-Apr-04 at 13:41

            I am trying to get a count of the most occurring words in my df, grouped by another Columns values:

            I have a dataframe like so:



            Answered 2022-Apr-04 at 13:11

            Your words statement finds the words that you care about (removing stopwords) in the text of the whole column. We can change that a bit to apply the replacement on each row instead:



            Import numpy can't be resolved ERROR When I already have numpy installed
            Asked 2022-Mar-23 at 20:13

            I am trying to run my chatbot that I created with python, but I keep getting this error that I don't have numpy installed, but I do have it installed and whenever I try to install it it tells me that it is already installed. The error reads "ModuleNotFoundError: No module named 'numpy'"

            I don't understand what the problem is, why is it always throwing this error? even for nltk and tensorflow even though I have them all installed.

            How can I resolve this issue?

            Here is a screen shot when i install numpy:

            Here is a screen shot of the error:



            Answered 2022-Mar-22 at 14:20

            This is not a very correct decision, but I had same problem with another libraries. You can be using different python interpreters (in my case it was anaconda) => libraries can be installed in different folders

            It was a temporarly solution, but I created new venv



            How to Capitalize Locations in a List Python
            Asked 2022-Jan-20 at 09:47

            I am using NLTK lib in python to break down each word into tagged elements (i.e. ('London', ''NNP)). However, I cannot figure out how to take this list, and capitalise locations if they are lower case. This is important because london is no longer an 'NNP' and some other locations even become verbs. If anyone knows how to do this efficiently, that would be amazing!

            Here is my code:



            Answered 2022-Jan-20 at 09:47

            What you're looking for is Named Entity Recognition (NER). NLTK does support a named entity function: ne_chunk, which can be used for this purpose. I'll give a demonstration:



            Manually install Open Multilingual Worldnet (NLTK)
            Asked 2022-Jan-19 at 09:46

            I am working with a computer that can only access to a private network and it cannot send instrunctions from command line. So, whenever I have to install Python packages, I must do it manually (I can't even use Pypi). Luckily, the NLTK allows my to manually download corpora (from here) and to "install" them by putting them in the proper folder (as explained here).

            Now, I need to do exactly what is said in this answer:



            Answered 2022-Jan-19 at 09:46

            To be certain, can you verify your current nltk_data folder structure? The correct structure is:



            tokenize sentence into words python
            Asked 2022-Jan-17 at 08:37

            I want to extract information from different sentences so i'm using nltk to divide each sentence to words, I'm using this code:



            Answered 2022-Jan-14 at 12:59

            First you need to chose to use " or ' because the both are unusual and can to cause any strange behavior. After that is just string formating:



            Convert words between part of speech, when wordnet doesn't do it
            Asked 2022-Jan-15 at 09:38

            There are a lot of Q&A about part-of-speech conversion, and they pretty much all point to WordNet derivationally_related_forms() (For example, Convert words between verb/noun/adjective forms)

            However, I'm finding that the WordNet data on this has important gaps. For example, I can find no relation at all between 'succeed', 'success', 'successful' which seem like they should be V/N/A variants on the same concept. Likewise none of the lemmatizers I've tried seem to see these as related, although I can get snowball stemmer to turn 'failure' into 'failur' which isn't really much help.

            So my questions are:

            1. Are there any other (programmatic, ideally python) tools out there that do this POS-conversion, which I should check out? (The WordNet hits are masking every attempt I've made to google alternatives.)
            2. Failing that, are there ways to submit additions to WordNet despite the "due to lack of funding" situation they're presently in? (Or, can we set up a crowdfunding campaign?)
            3. Failing that, are there straightforward ways to distribute supplementary corpus to users of nltk that augments the WordNet data where needed?


            Answered 2022-Jan-15 at 09:38

            (Asking for software/data recommendations is off-topic for StackOverflow; but I have tried to give a more general "approach" answer.)

            1. Another approach to finding related words would be one of the machine learning approaches. If you are dealing with words in isolation, look at word embeddings such as GloVe or Word2Vec. Spacy and gensim have libraries for working with them, though I'm also getting some search hits for tutorials of working with them in nltk.

            2/3. One of the (in my opinion) core reasons for the success of Princeton WordNet was the liberal license they used. That means you can branch the project, add your extra data, and redistribute.

            You might also find something useful at Obviously most of them are not for English, but there are a few multilingual ones in there, that might be worth evaluating?

            Another approach would be to create a wrapper function. It first searches a lookup list of fixes and additions you think should be in there. If not found then it searches WordNet as normal. This allows you to add 'succeed', 'success', 'successful', and then other sets of words as end users point out something missing.



            How do I turn this oddly formatted looped print function into a data frame with similar output?
            Asked 2022-Jan-12 at 06:34

            There is a code chunk I found useful in my project, but I can't get it to build a data frame in the same given/desired format as it prints (2 columns).

            The code chunk and desired output:



            Answered 2022-Jan-12 at 06:34

            Create nested lists and convert to DataFrame:



            Sagemaker Serverless Inference & custom container: Model archiver subprocess fails
            Asked 2021-Dec-16 at 16:11

            I would like to host a model on Sagemaker using the new Serverless Inference.

            I wrote my own container for inference and handler following several guides. These are the requirements:



            Answered 2021-Dec-14 at 09:30

            One possibility is that the serverless sagemaker version is trying to write the model in the same place that you have already wrote it in your inference container.

            Maybe review your custom inference code and don't load the model there.



            How to get a nested list by stemming the words inside the nested lists?
            Asked 2021-Dec-05 at 04:37

            I've a Python list with several sub lists having tokens as tokens. I want to stem the tokens in it so that the output will be as stemmed_expected.



            Answered 2021-Dec-05 at 04:37

            You can use nested list comprehension:



            No module named 'nltk.lm' in Google colaboratory
            Asked 2021-Dec-04 at 23:32

            I'm trying to import the NLTK language modeling module (nltk.lm) in a Google colaboratory notebook without success. I've tried by installing everything from nltk, still without success.

            What mistake or omission could I be making?

            Thanks in advance.




            Answered 2021-Dec-04 at 23:32

            Google Colab has nltk v3.2.5 installed, but nltk.lm (Language Modeling package) was added in v3.4.

            In your Google Colab run:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install nltk

            You can install using 'pip install nltk' or download it from GitHub, PyPI.
            You can use nltk like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            Do you want to contribute to NLTK development? Great! Please read for more details. See also how to contribute to NLTK.
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