nltk | NLTK the Natural Language Toolkit | Natural Language Processing library
kandi X-RAY | nltk Summary
kandi X-RAY | nltk Summary
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 nltk.org.
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Top functions reviewed by kandi - BETA
- 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 .
nltk Key Features
nltk Examples and Code Snippets
Lines of Code : 108License : Strong Copyleft (CC BY-SA 4.0)
Copywith open("somefile.txt") as infile:
data = infile.read().splitlines() # this seems to work OS agnostic
item = {
"title": data[0][4:],
"contents": [{"tag": line.split("##")[0], "sentence": line.split("##")[1]} for line in datas_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 Thedf['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 df["first_name"] = df['Name'].map(lambda x: x.split(' ')[0])
df["last_name"] = df['Name'].map(lambda x: x.split(' ')[1])
df["Text"] = (
df["Text"]
.str.lower()
.replace([r'\|', RE_stopwords], [' ', ''], regex=True)
.str.strip()
# .str.cat(sep=' ')
.str.split() # Previously .split()
)
Category Text
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
0 import nltk
import pandas as pd
nltk.download('stopwords')
nltk.download('punkt')
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.base import BaseEstimator, TransformerMixin
class TextNormalizer(BaseEstimator, TransformerMixin# download wordnet
import nltk
nltk.download('wordnet')
# import these modules
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet
nltk.download('wordnet')
lemmatizer = WordNetLemmatizer()
# choose some words to be 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 fopackage_data={'my_pkg' :['my_pkg/resources/nltk_data/*']}
Community Discussions
Trending Discussions on nltk
QUESTION
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:
...ANSWER
Answered 2022-Apr-04 at 13:11Your 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:
QUESTION
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:
...ANSWER
Answered 2022-Mar-22 at 14:20This 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
QUESTION
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:
...ANSWER
Answered 2022-Jan-20 at 09:47What 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:
QUESTION
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:
...ANSWER
Answered 2022-Jan-19 at 09:46To be certain, can you verify your current nltk_data folder structure? The correct structure is:
QUESTION
I want to extract information from different sentences so i'm using nltk to divide each sentence to words, I'm using this code:
...ANSWER
Answered 2022-Jan-14 at 12:59First 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:
QUESTION
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:
- 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.)
- 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?)
- Failing that, are there straightforward ways to distribute supplementary corpus to users of nltk that augments the WordNet data where needed?
ANSWER
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.)
- 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 http://globalwordnet.org/resources/global-wordnet-grid/ 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.
QUESTION
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:
...ANSWER
Answered 2022-Jan-12 at 06:34Create nested lists and convert to DataFrame:
QUESTION
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:
...ANSWER
Answered 2021-Dec-14 at 09:30One 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.
QUESTION
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.
ANSWER
Answered 2021-Dec-05 at 04:37You can use nested list comprehension:
QUESTION
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.
...ANSWER
Answered 2021-Dec-04 at 23:32Google 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
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Install nltk
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.
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