Natural Language Processing or NLP usually refers to a subfield of machine learning which allows a computer to platform analysis, manipulation, and human language generation with a complete understanding of it. The machine learning algorithms are extensively used in natural language processing with machine learning in Python.

Real-Time Applications of NLP

    The following are some of the important real-time NLP applications.

  • Retrieving information – similar and relevant search details in Google
  • Translation – content is translated from one language to another in Google translate
  • Analysing sentiments – user sentiment and people feelings are analyzed in Haters News
  • Filtering spams – the spam messages and emails are filtered separately in Gmail
  • Autocorrect – the misspelled words are corrected in Grammarly and Google keyboard
  • Answering questions – any query is answered by Quiz apps
  • Extracting data – events are structured from emails using Gmail
  • Simplifying text – the meaning of the sentences of simplified in Rewordify
  • Summarising text – sentence summary is obtained in smmry and Reddit’s autotldr
  • Automatic prediction – search results are predicted with more accuracy by Google
  • Recognition of speech – Google web speech and vocalware
  • Generation of natural languages – text can be generated from the video and images

What is the role of machine learning in NLP?

Large datasets are preferred for training the NLP models since it involves the analysis of huge documents. Supervised learning is used in place of training a machine learning model which is involved in analyzing the movie reviews and providing star ratings. The following are the important machine learning algorithms associated with different objectives.

  • Bayesian
    • Naive Bayes, Gaussian Naive Bayes, and Multinomial Naive Bayes
    • Bayesian networks, Averaged One – Dependence Estimators and Bayesian Belief Networks
  • Dimensionality reduction
    • Linear, Fixed, Quadratic, Regularized, and Mixed Discriminant Analysis
    • Partial Least Squares Discriminant Analysis, Projection Pursuit, and Principal Component Regression
    • Principal component analysis, Multidimensional Scaling and Partial Least Squares Regression
  • Clustering
    • K means and K medians
    • Hierarchical clustering and Expectation Maximization
  • Decision tree
    • C4. 5, C5. 0 and MS
    • Iterative Dichotomiser 3, Decision stump and Conditional Decision Trees
    • Chi-Squared Automatic Interaction Detection and Classification and Regression Tree
  • Instance-based
    • Locally weighted learning and self-organizing map
    • Learning vector quantization and k nearest neighbor
  • Deep learning
    • Stacked autoencoders and Deep Belief Networks
    • Convolutional Neural Networks and Deep Boltzmann Machine
  • Neural networks
    • Radial Basis Function Network and backpropagation
    • Hopfield network and Perceptron
  • Rule system
    • Cubist and Repeated Incremental Pruning to Produce Error Reduction
    • Zero rule and one rule
  • Ensemble
    • Random Forest, Boosting, and AdaBoost
    • Gradient Boosting Machines and Bootstrapped Aggregation
    • Stacked Generalization and Gradient Boosting Machines
  • Regularization
    • Ridge regression and Elastic Net
    • Least Angle Regression and Least Absolute Shrinkage and Selection Operator
  • Regression
    • Linear regression, stepwise and logistic regression
    • Locally estimated scatterplot smoothing and multivariate adaptive regression splines
    • Ordinary least square regression

Among the machine learning algorithms associated with natural language processing, maximum entropy, neural networks, support vector machines, Bayesian networks, and conditional random fields are the most popular ones.

Through this article, we provide you with a complete picture of natural language processing with machine learning in Python where we are about to start with the role played by machine learning in NLP!!!

Reason to choose to NLP machine learning in python programming

Our experts suggest taking up a hybrid approach which is an integration of rule-based systems and machine learning for your NLP research project. The following are the different types of NLP tasks

  • Low-level tasks
    • With the integration of machine learning and linguistic rules tokenization is established
    • Machine learning is used to make POS tagging
    • Rules are utilized for chunking while rules along with machine learning are used in determining sentence boundaries and syntax analysis
  • Mid-level tasks
    • The entities are determined using machine learning and who, where, and what based rules
    • Rules are used in establishing themes whereas integration of machine learning and descriptive rules are used to determine the NLP Thesis topics
    • Summarisation is conducted based on crisp rules
    • To establish intentions the machine learning algorithms and rules are used
    • Syntax matrix (extracting intent)
    • Machine learning (model training)
    • Linguistic rules (whitelisting and blacklisting words)
    • Multi-layered approach (increase accuracy)
  • High-level tasks
    • The machine learning algorithms and rules are integrated to be applied to sentiment analysis

By providing a complete comparative study on different real-time implemented projects in natural language processing and through case studies we will provide you with a deep insight into the advantages of NLP with machine learning algorithms.

For practical explanations on different mechanisms involved and processing models and NLP methodologies, you can contact our experts. We have gained more than 15 years of experience in guiding Natural Language Processing research projects. Let us now discuss the advantages of using Python for NLP

What is natural language processing with Python?

Natural language processing seems to be the method by which applications and services are developed in such a way to comprehend human languages. Google voice search and voice typing are the best examples of Machines being employed to understand human languages and make sentiment analyses. In this regard, it is quite important to know about Python which is very much useful in establishing NLP systems.

  • Object-based
    • Coding in Python can be done within the objects as it is an object-oriented programming language and hence the programming can be written easily
  • Interactive language
    • Interpreter can interact directly in order to establish python coding
  • Automatic interpretation
    • Python need not be compiled before execution as it is interpreted automatically
  • Easy to use
    • Beginners can easily understand the aspects of python
    • Also the supporting mechanism of python cover a large number of applications due to which it is easy to use for beginners

By gaining expertise due to the successful delivery of ample research projects in natural language processing to meet the growing demand for machine learning-based technologies, we can provide you with advanced project support in Natural Language Processing with machine learning in Python. Let us now talk about the working of Natural Language Processing systems

How does a natural language processing system work?

The following are all the steps involved in natural language processing systems working

  • Data collection and analysis
    • The algorithms involved in building natural Language Processing systems or involved in extracting useful patterns from the collected data and hence classifying them
  • Data pre-processing
    • Processing is involved in eliminating the unnecessary data and extracting useful features from the head using processes like text mining
  • Classifying and predicting
    • The NLP systems are mostly based on statistics and rules as a result of which probability and statistics gains importance in the field

As NLP and machine learning are a few of the rapidly evolving technologies, taking up projects in this field is one of the best choices since it is sure to give you scope for further research in the future. Interact with our technical experts for more detailed analysis on Natural Language Processing with machine learning in python. Let us not talk about the significance of python in NLP

Why Python is used in natural language processing?
  • Python has an inbuilt package for Natural Language Processing called as Natural Language toolkit or NLTK
  • Most probably the data is unstructured containing information in human-readable form
  • Hence data pre-processing is a there crucial step before programmed data analysis

In all these aspects python provides ready-to-use packages and advantages frameworks that can even be utilized by beginners efficiently. Our engineers have gained world-class certification and huge experience in handling Python projects by guiding in its development and deployment. So you can reach out to the device for any kind of guidance regarding the Python libraries. Let us now talk about implementing NLP using python,

How does Python implement NLP?

  • You can install the latest version of python 3 called python 3.7.1 for Mac, Linux, and Windows
    • You can directly download and install Python in Windows
    • Make use of the link that we provide in case of Mac OS
    • There are different comments for installing nltk packages into different Linux flavors
  • $sudo apt-get install python3 – minimal

This is the command used in installing python3 on Ubuntu Linux. You can find a large number of libraries for implementing NLP using python. Our technical team has got enough experience and knowledge in different types of NLP, the methodologies used algorithms, and real-time implementations. We will now look into the NLP Python library,

Python libraries for NLP

NLTK and SpaCy are the popular Python libraries for use in NLP. Our experts have gained huge knowledge in both of these powerful libraries.

  • NLTK library is suitable for beginner experiments and it is one of the important tools for academic and research
  • SpaCy library is used in advance the production-based applications and it is faster as Cython is used in its programming

In the following let us have a look into different Python libraries for Natural Language Processing, their features, and use cases

  • SpaCy
    • It is an open-source Python library for NLP which is fast and easy to use
    • Word vectors, dependency parsing, classification, tokenization, POS tagging, named entity recognition, and sentiment analysis are its features
    • Auto correct, review analysis, summarization and autocomplete are its use cases
  • NLTK
    • Tokenization, classification, sentiment analysis, POS tagging, recognition of named entity, and chatbot packages are its features
    • Sentiment analysis, chatbots building, and recommendation system are its use cases
  • Gensim
    • It is used in detecting similarities and modeling topics which is probably involved in handling multiple tasks
    • TF – IDF, Non-Negative Matrix factorization, and the latent semantic analysis are its features
    • Document to vector conversion, summarization of text, and detecting similarity in the text are its use cases
  • Pattern
    • It is one of the important NLP Python frameworks consisting of crisp syntax and can be used in handling scientific and non-scientific tasks
    • Tokenization, parsing, sentiment analysis, POS tagging, and named entity pattern recognition are some of its features
    • Spelling auto-correction, optimisation of search engines, and sentiment analysis are its use cases
  • TextBlob
    • It is an important Python library used in the text data processing
    • Parsing, POS tagging, translation of languages, integrating wordnets, sentiment analysis, and extraction of noun phrases or their features
    • Analysis of sentiment spell correction detecting languages and translation are its use cases

You can get high-level, interpretative, and minute details of all these Python libraries from our website. Python is perhaps one of the world’s top programming languages which are being used rapidly these days. You can feel free to contact us regarding any expert tips in using Python for NLP projects. Let us now look into the use of Tensorflow in NLP tasks

Tensorflow for advanced NLP tasks

  • Tensorflow is a deep learning framework that makes use of machine learning and neural networks
  • Customised NLP algorithms can be recreated at an advanced level using the deep learning features of TensorFlow

Therefore TensorFlow is one of the crucial aspects to be known for a researcher in NLP. Talk to our experts about the use of Tensorflow and other python libraries. We will provide all details regarding hyperparameters configuration and associated computation methods. Let us talk about starting NLP projects using python

How to start for NLP using python?

  • A popular Python library called natural language toolkit (NLTK) is used in implementing NLP
  • Most often it is regarded as the easiest NLP library that a researcher can use

You can get in touch with us regarding project design, command execution, and code implementation support. Our technical support team is ready to help you in all aspects of your natural language processing project with machine learning in Python. Here is an expert answer to one of the frequently asked questions regarding the use of python NLTK

How do I use NLTK in Python?

Data pre-processing is an important step before you attempt to analyze the unstructured and human-readable data using machines. Our experts are here to help you out in creating visualizations and perform the fundamental text analysis necessary for text pre-processing. The following are some of the tips in using the python NLTK library for text analysis,

  • $ pip install nltk
    • This program can be implemented in Windows, Linux, and Mac to install the natural language toolkit of python 2.7, 3.4 and 3.5
  • Import nltk
    • This code can be used to check the proper installation of nltk into python
  • Import nltk

  • After installing the natural language toolkit, the packages associated with can be run by using the above code

For necessary assistance and essential benchmark references for your research data natural language processing with machine learning in Python, you can contact us at any time. Further, a deep insight into the installation procedure will be provided to you by our world-class certified python developers.