Filtering English nouns from noisy text using natural language processing (NLP) and spell checking is a technique that can help to extract meaningful information from unstructured text data. Noisy text data can be found in many areas, such as social media posts, online reviews, and customer feedback. This technique can help to identify and extract English nouns from this noisy text, which can be useful for sentiment analysis, content analysis, and other natural language processing tasks.
Enchant is a Python library that provides a simple interface for working with various spell-checking engines, including the Hunspell engine used by default in many Linux distributions. Enchant allows the code to check whether a given word is an English word. Enchant helps improve the code's accuracy by filtering out non-English words, which reduces noise in the data and ensures that only valid English nouns are included in the output.
Using NLP and spell-checking to filter out non-English words and identify only English nouns can help improve the efficiency and accuracy of natural language processing tasks. This can benefit various industries, from marketing and advertising to customer service and product development. Overall, this technique has the potential to help organizations make better-informed decisions based on the insights extracted from their unstructured text data.
Here is an example of how to Filter English Nouns from Noisy Text using NLP and Spell Checking:
Preview of the output that you will get on running this code from your IDE
In this solution we use the enchant function of python library.
import spacy, re import enchant #pip install pyenchant d = enchant.Dict("en_US") nlp = spacy.load("en_core_web_sm") sentence = "For example, it filters nouns like motorbike, whoosh, trolley, metal, suitcase, zip etc" cleanString = re.sub('[\W_]+',' ', sentence.lower()) # Merging \W and _ into one regex doc= nlp(cleanString) for token in doc: if token.pos_=="NOUN" and d.check(token.text): print (token.text) # => [example, nouns, motorbike, whoosh, trolley, metal, suitcase, zip]
- Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
- Enter the Text
- Run the file to remove the meaningless words in the text.
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I found this code snippet by searching for "Meaningless spacy Nouns " in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created and tested in Vscode version 1.75.1
- The solution is created in Python 3.5.17 Version
- The solution is tested on Spacy 3.4.3 Version
Using this solution, we are able to find Meaning full Nouns using spacy library. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us remove the meaningless nouns in python