![]() It uses the NLTK library to get synonyms for each word in the text and replaces them with a randomly chosen synonym. This function takes in a text string and the number of synonym replacements to perform (default is 5). join (words ) # Print the original and modified sentences print ( "Original sentence:", sentence ) print ( "Modified sentence:", new_sentence ) Words = synonyms # Join the modified words back into a sentence name ( ) ) # Replace the word with a random synonym, if available if len (synonyms ) > 0 : word_tokenize (sentence ) # Loop through each word in the sentence for i, word in enumerate (words ) : # Get the synonyms for the word Sentence = "The quick brown fox jumps over the lazy dog" # Tokenize the sentence into words Please note that you need to have the necessary Python libraries installed in your Python environment to run this code. Here's an example of how to implement synonym replacement using the NLTK library in Python (which you can install with pip install nltk): This generates new negative reviews that have a slightly different wording but still convey the same sentiment. For example, you can replace the word "bad" with "poor" or "terrible". One technique is to use synonym replacement where you replace certain words in the negative reviews with their synonyms. To address this issue, you can use data augmentation techniques to generate synthetic negative reviews from the existing ones. ![]() However, the dataset is imbalanced with a lot more positive reviews than negative ones. Suppose you have a dataset of text reviews for a product and you want to classify them as positive or negative. Here's an example of how data augmentation can be used in text data: Run the compile command to generate the MO files: $ pybabel compile -d locale -l itĬompiling catalog locale/it/LC_MESSAGES/messages.po to locale/it/LC_MESSAGES/messages.moĬreate a file named app.Data augmentation can be applied to various types of data beyond images, audio, and video. Now initialize the Italian translations using the init command and provide the translations: $ pybabel init -d locale -l it -i locale/messages.potĬreating catalog locale/it/LC_MESSAGES/messages.po based on locale/messages.pot " There are %(count)s %(name)s servers.\n" "Content-Type: text/plain charset=utf-8\n" "PO-Revision-Date: YEAR-MO-DA HO:MI ZONE\n" # This file is distributed under the same license as the PROJECT project. That will generate the following catalog: # Translations template for PROJECT. Invoke the following command to extract the base messages: ¢ pybabel extract -F babel-mapping.ini -o locale/messages.pot. Instead of translating, for example, day names or month names for a particular language or script, you can make use of the translations provided by the locale data included with Babel based on CLDR data.Ĭreate a file name loc.py and add the following code: from babel import Locale (CLDR) Unicode Common Locale Data Repository is a standardized repository of locale data used for formatting, parsing, and displaying locale-specific information. If you don't have pip installed, you can get it with easy_install $ sudo easy_install pip Working with Locale Data Installing Babel is simple using pip $ pip install Babel Both aspects aim to help automate the process of internationalizing Python applications as well as provide convenient methods for accessing and using this data.īabel, in essence, works as an abstraction mechanism for a larger message extraction framework, as you can extend it with your own extractors and strategies that are not tied to a particular platform. The second one is the usage of CLDR (Common Locale Data Repository) to provide formatting methods for currencies, dates, numbers, etc. The first is the gettext module that uses gettext to update, extract, and compile message catalogs and manipulate PO files. ![]() About Babelīabel provides internationalization (python i18n) and localization (python l10n) helpers and tools that work in two areas. You can also find the code described in this tutorial on GitHub. We are also going to see how to integrate it with Jinja2 templates and how to integrate Phrase's in-context editor into a Flask application to help with the translation process by simply browsing the website and editing texts along the way, making your Python localization process a lot simpler. We are going to extend our knowledge by using Babel and see some practical examples of its usage in python i18n. We have given detailed tutorials on gettext tools and integrating gettext with Python. ![]()
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