NLTK | An Animal Crossing New Leaf Save Editor & Toolkit | Hacking library
kandi X-RAY | NLTK Summary
kandi X-RAY | NLTK Summary
NLTK is a WIP ACNL Toolkit without the hassle of taking out your SD card. Only Homebrew or CFW is needed to use it. The main developers of this project are Slattz and Cuyler.
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QUESTION
I have a folder that contains a group of files, and each file contains a text string, periods, and commas. I want to replace the periods and commas with spaces and print all the files afterwards.
I used Replace, but this error appeared to me:
...ANSWER
Answered 2021-Jun-11 at 10:28It seems you are trying to use the string function "replace" on a list. If your intention is to use it on all of the list's members, you can do it like so:
QUESTION
I have this project.
I have a folder called "Corpus" and it contains a set of files. It is required that I delete the "stop words" from these files and then save the new files that do not contain the stop words in a new folder called "Save-files".
And when I opened the “Save-Files” folder, I saw inside it the files that I had saved, but they were without content, that is, when I open the number one file, it is empty without content.
And as it is clear in the first picture, here is the “Save-Files” folder, and inside it there is a group of files that i saved.
And when I open any of the files, it is empty.
How can I solve the problem?
...ANSWER
Answered 2021-Jun-10 at 14:10you need to update the line to read the file to
QUESTION
data = ("Thousands of demonstrators have marched through London to protest the war in Iraq and demand the withdrawal of British troops from that country. Many people have been killed that day.",
{"entities": [(48, 54, 'Category 1'), (77, 81, 'Category 1'), (111, 118, 'Category 2'), (150, 173, 'Category 3')]})
...ANSWER
Answered 2021-Jun-09 at 02:30Not sure if the final format is json, yet below is an example to process the data into the print format, i.e.
QUESTION
I'm running several machine learning models to find the one which the highest accuracy score, however, all the accuracy scores are the exact same. I performed NLP on social media text and I'm training my models to tag sentiment based on sentiment determined from NLTK.
I'm using the same training and test sets, but I've done this method before in the past and received different scores on different models. Why are all of mine the same? Am I overfitting perhaps?
Here is my code where I'm splitting and training:
...ANSWER
Answered 2021-Jun-08 at 00:47I'm not sure what is the cause of the problem, but since the output of you SVM model and DecisionTreeClassfier always output 1, I suggest you try a more complex model like RandomForestClassifier and see what it comes out.
I've similar experience before, no matter how I tuned the training hyperparameters, the model always give the same performance metric -- this may cause by 2 probabilities:
- Our data is not suitable for the model, for example all values in the vector is zero: [0, 0, 0, 0, 0, 0, 0]
- Our model is too simple, which could only perform linear modeling, so that it could not learn too complex mapping function.
Since your SVM is built with linear kernel, could you try an more complex model and see what it comes out? And could you examine that if your X_train_vectors is all zero's in the matrix?
QUESTION
I am running a NLP program in which I do a text preprocessing before running the main algorithms. The preprocessing is simple: I have an array of very long strings (around 20K words each string, 30K strings in total). I want to tokenize on each string with nltk.stem.porter.PorterStemmer
:
ANSWER
Answered 2021-Jun-06 at 12:35Where speed is a concern, SpaCy is often preferable over NLTK. It offers both batch processing as well as GPU integration.
With an iterable of strings, this is the basic procedure of how you'd perform batch processing (note that there are a lot of options to tweak, like disabling certain parts of the pipeline that you don't need and setting a batch size, all of which is explained in detail in the SpaCy docs).
QUESTION
I have recently sourced and curated a lot of reddit data from Google Bigquery.
The dataset looks like this:
Before passing this data to word2vec to create a vocabulary and be trained, it is required that I properly tokenize the 'body_cleaned' column.
I have attempted the tokenization with both manually created functions and NLTK's word_tokenize, but for now I'll keep it focused on using word_tokenize.
Because my dataset is rather large, close to 12 million rows, it is impossible for me to open and perform functions on the dataset in one go. Pandas tries to load everything to RAM and as you can understand it crashes, even on a system with 24GB of ram.
I am facing the following issue:
- When I tokenize the dataset (using NTLK word_tokenize), if I perform the function on the dataset as a whole, it correctly tokenizes and word2vec accepts that input and learns/outputs words correctly in its vocabulary.
- When I tokenize the dataset by first batching the dataframe and iterating through it, the resulting token column is not what word2vec prefers; although word2vec trains its model on the data gathered for over 4 hours, the resulting vocabulary it has learnt consists of single characters in several encodings, as well as emojis - not words.
To troubleshoot this, I created a tiny subset of my data and tried to perform the tokenization on that data in two different ways:
- Knowing that my computer can handle performing the action on the dataset, I simply did:
ANSWER
Answered 2021-May-27 at 18:28First & foremost, beyond a certain size of data, & especially when working with raw text or tokenized text, you probably don't want to be using Pandas dataframes for every interim result.
They add extra overhead & complication that isn't fully 'Pythonic'. This is particularly the case for:
- Python
list
objects where each word is a separate string: once you've tokenized raw strings into this format, as for example to feed such texts to Gensim'sWord2Vec
model, trying to put those into Pandas just leads to confusing list-representation issues (as with your columns where the same text might be shown as either['yessir', 'shit', 'is', 'real']
– which is a true Python list literal – or[yessir, shit, is, real]
– which is some other mess likely to break if any tokens have challenging characters). - the raw word-vectors (or later, text-vectors): these are more compact & natural/efficient to work with in raw Numpy arrays than Dataframes
So, by all means, if Pandas helps for loading or other non-text fields, use it there. But then use more fundamntal Python or Numpy datatypes for tokenized text & vectors - perhaps using some field (like a unique ID) in your Dataframe to correlate the two.
Especially for large text corpuses, it's more typical to get away from CSV and instead use large text files, with one text per newline-separated line, and any each line being pre-tokenized so that spaces can be fully trusted as token-separated.
That is: even if your initial text data has more complicated punctuation-sensative tokenization, or other preprocessing that combines/changes/splits other tokens, try to do that just once (especially if it involves costly regexes), writing the results to a single simple text file which then fits the simple rules: read one text per line, split each line only by spaces.
Lots of algorithms, like Gensim's Word2Vec
or FastText
, can either stream such files directly or via very low-overhead iterable-wrappers - so the text is never completely in memory, only read as needed, repeatedly, for multiple training iterations.
For more details on this efficient way to work with large bodies of text, see this artice: https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/
QUESTION
In my simple for loop below, I iterated over 3600 texts, tokenized them and saved them into a list:
...ANSWER
Answered 2021-Jun-04 at 12:54You collect document tokens into a list of lists, list
(list.append(tokenize)
), and then in a for tokens in list:
loop you assign the most_common()
value to the most_common
variable thus getting only the most common terms for the last document only.
I sugges to .extend
the list of tokens to collect all tokens into a single flat list of words, and then getting the top 10 most common tokens by passing the list to the nltk.FreqDist()
method:
QUESTION
There are some parts of the nltk corpus that I'd like to add to the setup.py file. I followed the response here by setting up a custom cmdclass
. My setup file looks like this.
ANSWER
Answered 2021-Jun-03 at 12:13Pass the class, not its instance:
QUESTION
I am doing some NLP with NLTK and I have a Counter() sequences, for example
...ANSWER
Answered 2021-Jun-03 at 08:24As metatoaster has elaborated in his comment, you would prob. have to restructure your data to perform the operation in the exact way you want (without O(n)).
That being said, in the current state and with reference to your example, you could do:
QUESTION
As the title clearly describes the issue I've been experiencing, no Pipfile.lock
is being generated as I get the following error when I execute the recommended command pipenv lock --clear
:
ANSWER
Answered 2021-Jun-03 at 06:29By looking at the pypi
site for keras-nightly
library, I could see that there are no versions named 2.5.0.dev
. Check which package is generating the error and try downgrading that package.
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