Python Natural Language Processing Libraries: Automated tools using NLP in 2022

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5 Best Python Natural Language Processing Libraries

A company can gain valuable insights, patterns, and solutions through Natural Language Processing. Most fields and domains have implemented Python. It is one of the most popular languages.

5 Best Python Natural Language Processing Libraries

What is Natural Language Processing?

An area of artificial intelligence known as Natural Language Processing investigates how machines integrate natural language into communication with people like Spaniards, English speakers, or Chinese speakers.

Within artificial intelligence and applied linguistics, natural language processing studies human-machine interactions using natural language.

As a particular example, it focuses on the separation and identification of relevant components of human communications. This program aims to give machines the ability to understand, interpret, and manipulate human language.

The most popular NLP utility is the virtual assistant or chatbot, but they are not the only ones.

Also, it is important to know that NLP does not give a chatbot intelligence, but rather it gives it the ability to process and generate human language. Using rules or neural networks will give your virtual assistant intelligence.

Almost any language spoken by humans can be processed by computers. Due to limitations of economic value or practical use, only the most widely used digital languages have applications.

Writing (text), speaking (voice) and sign language are just some of the ways in which human languages may be expressed. NLP is naturally more advanced in word processing because there is much more data and it is easier to get it in electronic form.

In order to understand the question, an audio file, even if it is digital, must be transformed into characters or letters first. As a response, we synthesize a sentence and then elaborate on it.

In any case, the artificial voice is sounding more and more human, as it adopts tonal and prosodic inflections from human speech.

Machines will be able to read and understand human languages, to put it simply.

Why Python for Natural Language Processing?

Why Python for Natural Language Processing?

Languages like Python can be used to process linguistic data with ease since these languages are simple and powerful in their own right.

Python is a good choice because it has a shallow learning curve and is well structured. Its string handling is good, and it is transparent.

Interactive exploration is facilitated by Python’s interpreted nature. Python allows you to encapsulate and reuse data and methods easily because it is an object-oriented language.

In addition to allowing attributes and variables to be added on the fly, Python is a dynamic language that facilitates rapid development.

Hire Python Developers

In addition to its extensive standard library, Python comes with components that integrate graphics, numerical processing, and web connectivity.

There have been extensive uses of Python in industry, scientific research, and education across the globe.

Many software developers praise Python’s ability to facilitate productivity, quality, and maintainability.

Also Read: Python vs. Java: Uses, Performance, Learning

Best Natural Language Processing Libraries

In the natural language processing field, there are many libraries that support this process. A library with a variety of functions allows a computer to understand natural language by dividing text according to syntax, extracting helpful phrases, and removing unnecessary words. We have compiled a list of the top NLP libraries for Python in this article. You may even be able to use these libraries to create your own natural language processing project.

1.  NLTK

A Python NLP program can be built using NLTK’s infrastructure. There are standard classes to represent various aspects of natural language processing, for example, part of speech tags, syntactic parsing, and text classification, as well as standard implementations of these tasks that can be combined to solve complex problems.

Developing applications related to human language is most commonly done using the Natural Language Toolkit. NLTK contains several libraries for performing text functions including derivation, tokenization, classification, analysis, and semantic thinking.

After installing NLTK, you should run the following code to install NLTK packages. Then, you can use them as required:

importnltk
nltk.download()

With this, you can choose what packages should be installed through the NLTK downloader.

NLTK Downloader

One of the most important features of the NLTK is that it is free and is open source. For industrial-level projects, this toolkit is slow, but it is ideal for those starting out in natural language processing.

Although it has a steep learning curve, you may need to spend some time familiarizing yourself with it.

1.  TextBlob

The TextBlob Python library was created specifically for text data processing and natural language processing. It provides functions such as

  • tokenization
  • sentiment analysis
  • noun phrase extraction
  • spell checking
  • translation
  • stemming
  • classification
  • tagging of parts of speech

As TextBlob is based on both NLTK and Pattern, it is very easy to integrate with both of them.

A great tool for understanding NLP’s complexities and prototyping, TextBlob is a great choice for beginners. In terms of industrial-level NLP production, this library is far too slow to be useful.

The installation of TextBlob requires none more than opening the anaconda prompt (or terminal if you’re using Mac OS or Ubuntu) and entering the following commands:

pip install -U textblob

Run the following command to download the necessary corpora:

python -m textblob.download_corpora

Beginners should start by learning this library, then for more advanced work, you can learn Spacy.

3. spaCy

The spaCy library is a natural language processing library developed in Python so that it can be used in industrial projects and provide useful information. The software is written in memory-managed Cython, so it runs very quickly. The company’s website claims that its natural language processing is the fastest in the world using Ruby on Rails.

spaCy

SpaCy offers various NLP features, such as

  • dependency analysis
  • tokenization
  • sentence segmentation using syntax
  • named entity recognition
  • speech tagging

It can be utilized to build complex NLP models in Python, as well as integrating with other Python libraries – like TensorFlow, Scikit-Learn, PyTorch, etc.

The spaCy Open-Source NLP Library provides several built-in features for Natural Language Processing (NLP) in Python. Processing and analyzing data with it have become increasingly popular in NLP.

It’s important to analyze unstructured textual data to gain insights. In order to accomplish this, you need to provide your data in a format that computers understand.

Installing spaCy with pip, a Python package manager, is easy.

pip install spacy

A variety of models are offered by SpaCy. In English, the default model is en_core_web_sm.

To download the English language models and data, you need to do the following:

python -m spacy download en_core_web_sm

Because of its speed, ease-of-use, accuracy, and extensibility, spaCy is gaining huge popularity for NLP applications.

1.  Genism

Gensim describes itself as a program that does ‘Topic Modelling for Humans’. However, there is much more to it than that.

The topic modeling technique is used to extract the important topics from large quantities of text. In addition to algorithms like LDA and LSI, Gensim provides the sophistication necessary for designing high-quality topic models.

Other packages, such as scikit, R, etc, provide topic models and word embeddings. But Gensim also has many more convenient text processing facilities and a wide range of facilities for building and evaluating topic models.

The package allows users to process texts, work with word vector models (like Word2Vec) and build topic models.

Furthermore, another significant advantage of Gensim is its ability to handle large documents without needing to load them all into memory.

Run the following command to install Gensim:

pip install –-upgrade genism

By using data-streamed algorithms, Gensim can handle large corpora. RAM size limitations do not apply to datasets.

The Python extensions for Gensim work with all Python versions that aren’t end-of-life.

2.  CoreNLP

CoreNLP can be used to generate quite complete NLP pipelines with very little coding.

It contains pre-built methods for each of the most frequent NLP processes, from Part of Speech (POS) tagging, to Named Entity Recognition (NER), to Dependency Parsing, to Sentiment Analysis. Other languages than English are also supported, including Arabic, Chinese, German, French, and Spanish.

CoreNLP is built around core NLP pipelines. Pipelines take input texts and process them, resulting in the output object core document.

Custom and adaptable core NLP pipelines are available for NLP implementations. Annotators can be added, removed, or edited using the properties object.

Also Read: Java Vs .Net Vs Python – Which One Is Best to Choose?

Some examples of NLP Implementations

Listed below are some examples of how NLP has been implemented:

Grammar Checker and Spell Checker:

In natural language processing, this is perhaps one of the most commonly used applications. Grammarly, for example, provides a wide range of features that can help a writer write better content.

Grammar Checker and Spell Checker

A beautiful piece of literature can be created from any ordinary piece of text. You will need these helpful friends regardless of whether you’re writing to your boss, writing a report, or writing an article.

Paraphrasing or Article Rewriting tools

Using Natural Language Processing has been a great way to produce new content from old material for quite some time. Paraphrasing, rephrasing, and rewriting are terms used to describe this process. Bloggers and writers greatly benefit from a unique content-generating paraphrasing tool.

Plagiarism Checker or Similarity Checker

Plagiarism Checker or Similarity Checker

Using a semantic-analysis-based plagiarism checker, plagiarism can be detected between two or more words, and whether their meanings are similar or different. When a comparison is performed, it is determined that the smaller the value of the comparison algorithm, the closer the words are to each other.

 

NLP-based tools for automated development

Automation in healthcare

Patients benefit from the use of voice-activated assistants and speech-recognition platforms that are being used by hospitals and clinics.

Using this technology, patients can decrease their waiting times, gain greater access to their information, and avoid unnecessary costs and delays.

Automation in contact centers

A customer calls a customer service center. Upon receiving a call, an automated voice will ask why you’re calling. When asked a question about their account or if there was an issue with the product, the customer replies, “I have a question about your product.”.

Through Natural Language Processing, a voice system can understand what a customer is saying and route the call to the appropriate customer service representative. Customers receive a fast response and can use conversational language to communicate.

In making business decisions

The use of technology for marketing goals can help companies earn more money with NLP. To accomplish this, marketers can create chatbots that appear whenever consumers visit their websites.

After gathering visitor information, the bot would use that information to send targeted advertisements and content to these visitors.In addition to understanding the language of social media and the sentiment behind it, NLP can play a useful role in marketing.

The use of NLP gives businesses insight into their marketing campaigns, as well as consumer sentiment related to their brand and products. Using this information, they can make informed decisions on how to move forward based on what the public thinks about them.

Can be helpful in the educational field

Learning can also be improved through NLP in the classroom. Google Docs’ Writing Mentor program, for example, can help students with their writing. In this Google add-on, students can receive feedback on their writing as they develop a topic, make decisions about coherence, choose words, and more.

 

Last Words

5 Best Python Natural Language Processing Libraries

In various domains, the Python ecosystem provides libraries, frameworks, and modules that are diverse. Natural language processing and text analysis frameworks and libraries are available for installation and use just like other modules built into the Python standard.

It has taken a long time to build these frameworks, and they may still be developed after years of use. It is sometimes useful to see how active the framework’s community is when evaluating it.

There are many methods, capabilities, and functions available in each framework that operate on text, prepare data for further analysis and obtain information. They use pre-processed textual data to apply machine learning algorithms.

It saves a great deal of time and effort that would have been spent on writing standard code that handles, processes, and manipulates text data. Consequently, developers and researchers can concentrate more on the actual problem and on the logic and algorithms necessary to solve it.

It is possible to extract meaning from unstructured data collected from conversations as well as social media and email by using natural language processing. The use of natural language processing can help many industries such as healthcare, education, and business.

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