word_tokenize | Vietnamese Word Tokenize | Natural Language Processing library

by undertheseanlp Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | word_tokenize Summary

word_tokenize is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. word_tokenize has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Vietnamese Word Tokenize

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word_tokenize has a low active ecosystem.

It has 40 star(s) with 22 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 113 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of word_tokenize is current.

Quality

word_tokenize has 0 bugs and 0 code smells.

Security

word_tokenize has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

word_tokenize code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

word_tokenize does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

word_tokenize releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

word_tokenize saves you 463 person hours of effort in developing the same functionality from scratch.

It has 1092 lines of code, 61 functions and 38 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed word_tokenize and discovered the below as its top functions. This is intended to give you an instant insight into word_tokenize implemented functionality, and help decide if they suit your requirements.

Convert a sentence into a dictionary
Checks if the word contains a digit
Check if a word is a number
Checks if word contains punctuation
Train and test features
Predict a sentence
Get tokenizer
Returns a dictionary of feature features
Train the model
Evaluate the input
Evaluate text
Counts the number of chunks in the file
Convert raw data into TaggedCorpus
Processes a text file
Convert a WordPattern to WordPattern
Downsample the train
Reads a corpus
Returns a TaggedCorpus object
Load a corpus from a file
Create regex patterns
Tokenize a sentence
Convert a word to a regular expression
Get the tokenizer
Parse arguments
Train the full model
Iterate over features

Get all kandi verified functions for this library.

word_tokenize Key Features

No Key Features are available at this moment for word_tokenize.

word_tokenize Examples and Code Snippets

No Code Snippets are available at this moment for word_tokenize.

Community Discussions

Trending Discussions on word_tokenize

Get first element of tokenized words in a row

Get a word's function in a sentence PY

extract keyword from sentences in a pandas text column, using nltk, and or regex, and place words in another column as groups from a sentence

zipfile.LargeZipFile: Filesize would require ZIP64 extensions

Python read in collection of xml files to df or dict

Process input data to a correct format for a custom NER BERT model

Combine two regexp grammars in nltk

How to Capitalize Locations in a List Python

use output of previous magrittr chains as arguments to further arguments

tokenize sentence into words python

QUESTION

Get first element of tokenized words in a row

Asked 2022-Apr-04 at 16:44

Using the existing column name, add a new column first_name to df such that the new column splits the name into multiple words and takes the first word as its first name. For example, if the name is Elon Musk, it is split into two words in the list ['Elon', 'Musk'] and the first word Elon is taken as its first name. If the name has only one word, then the word itself is taken as its first name.

A snippet of the data frame

Name Alemsah Ozturk Igor Arinich Christopher Maloney DJ Holiday Brian Tracy Philip DeFranco Patrick Collison Peter Moore Dr.Darrell Scott Atul Gawande Everette Taylor Elon Musk Nelly_Mo

This is what I have so far. I am not sure how to extract the name after I tokenize it

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ANSWER

Answered 2022-Apr-04 at 16:44

Try this snippet:

Source https://stackoverflow.com/questions/71740706

QUESTION

Get a word's function in a sentence PY

Asked 2022-Mar-29 at 12:19

my question is a bit tricky here, in fact i'm trying to identify the ROLE of a word in a given sentence, i manage to get something using nltk, the problem is that it's telling me what the word is, what i'm searching for is it's job. For example God Loves Apples would not return God as a subject in this given sentence. in fact here it would return God as a NNP, which is not what i'm looking for. So im looking for getting as the dict key the role of the given word in it's string (looking for god as subject not god as NNP)

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ANSWER

Answered 2022-Mar-29 at 12:19

You could use dependency parsing. NLTK is not ideal for this task, but there are alternatives like CoreNLP or SpaCy. Both can be tested online (here and here). The dependency tree will tell you that in God loves apples., the token God is connected to the main verb with the nsubj relation, i.e., nominal subject.

I usually go for SpaCy:

Source https://stackoverflow.com/questions/71661707

QUESTION

extract keyword from sentences in a pandas text column, using nltk, and or regex, and place words in another column as groups from a sentence

Asked 2022-Feb-05 at 12:45

A pandas data frame of mostly structured data has 2 columns containing user input, text narratives. Some narratives are poorly written. I'm looking to extract keywords that occur in the same sentence within each narrative. The words are sometimes bigrams (fractured implant) but usually lots of non-keywords are in-between the keywords (implant was really fractured). They are only a pair if they occur in the same sentence within the narrative, and it's possible to have more than 2 keywords in a sentence. Here's an example, plus my attempt.

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ANSWER

Answered 2022-Feb-05 at 12:45

You could try tokenizing the text before extracting the keywords:

Source https://stackoverflow.com/questions/70995812

QUESTION

zipfile.LargeZipFile: Filesize would require ZIP64 extensions

Asked 2022-Feb-04 at 12:35

I am creating an Excel file and writing some rows to it. Here is what I have written:

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ANSWER

Answered 2022-Feb-04 at 12:35

The issue is caused by the fact that the resulting file, or components of it are greater than 4GB in size. This requires an additional parameter to be passed by xlsxwriter to the Python standard library zipfile.py in order to support larger zip file sizes.

The answer/solution is buried in the exception message:

Source https://stackoverflow.com/questions/70985158

QUESTION

Python read in collection of xml files to df or dict

Asked 2022-Feb-03 at 13:11

I have a collection of xml files that I would like to read in to either a dataframe (df) or a dictionary (dict). Each xml file has the same format with regard to the classes.

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ANSWER

Answered 2022-Feb-03 at 13:11

You can use some library such as xmltodict or write your own parser. From xmltodict readme:

Source https://stackoverflow.com/questions/70971724

QUESTION

Process input data to a correct format for a custom NER BERT model

Asked 2022-Feb-02 at 17:14

I want to train a custom NER BERT model. Therefore I need to process my input data in a certain way.

My df_input looks like this:

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ANSWER

Answered 2022-Feb-02 at 16:31

This should be pretty fast:

Source https://stackoverflow.com/questions/70959079

QUESTION

Combine two regexp grammars in nltk

Asked 2022-Jan-27 at 21:28

I'm defining a noun phrase using grammar in nltk. The example provided by nltk is:

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ANSWER

Answered 2022-Jan-27 at 21:28

You can just define two NP rules in one grammar:

Source https://stackoverflow.com/questions/70880940

QUESTION

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:

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ANSWER

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:

Source https://stackoverflow.com/questions/70774817

QUESTION

use output of previous magrittr chains as arguments to further arguments

Asked 2022-Jan-18 at 17:01

if I have the following example:

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ANSWER

Answered 2022-Jan-18 at 16:51

I don't know if there's a cleaner or more efficient way to do this, but what I usually do in this situation is to nest piplines at the highest level where I need to pull an input from and pipe in the output using . to continue the chain.

Source https://stackoverflow.com/questions/70759057

QUESTION

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:

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ANSWER

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:

Source https://stackoverflow.com/questions/70710646

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

Vulnerabilities

No vulnerabilities reported

Install word_tokenize

You can download it from GitHub.
You can use word_tokenize 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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