In What Ways Does NLP Make Use of ML?

The advances in natural language processing (NLP) have been remarkable, and it has now become an integral part of our lives. It has revolutionised how we carry out tasks that were previously time consuming, such as giving voice commands to our smartphones, virtual home assistants, and even vehicles. This is a testament to the prominent role of NLP and machine learning (ML) in the development of voice-enabled technologies such as Google Assistant, Alexa, and Siri, which have become commonplace in our homes and workplaces.

This paper will offer an overview of Natural Language Processing (NLP), highlighting its relationship to Machine Learning, the most promising NLP libraries, and the potential for future development through Deep Learning. By exploring these areas, we can gain a better understanding of the possibilities for NLP and its potential impact on the future. NLP is a subfield of Artificial Intelligence (AI) that focuses on the communication between computers and human language. It is a powerful tool for analysing and understanding large amounts of unstructured data, such as text documents, audio recordings, and images. NLP is closely related to Machine Learning, which involves using algorithms to identify patterns and extract insights from data. Through the application of ML algorithms, NLP can identify and classify text, uncover relationships between words and phrases, and generate new text based on existing data. By combining NLP and Machine Learning, organisations can gain valuable insights and make better decisions. Furthermore, there are a plethora of NLP libraries available for developers and data scientists to use for their projects, such as spaCy, NLTK, and Gensim. Each of these libraries provides a range of tools to help developers create powerful applications and models. Finally, Deep Learning has opened up new possibilities for NLP, allowing us to explore vast amounts of data and generate more accurate models. With its potential to revolutionise the way we process natural language, Deep Learning is sure to be a major contributor to the future of NLP.

Human communication is an important factor to consider when discussing natural language. However, it is a complex challenge for robots to interpret this type of language, as there are many aspects that influence human interaction. These elements can include the language, dialect, conversational context, and relationship between the speakers, all of which can lead to different rules and interpretations. Therefore, it is necessary to take into account the various elements of human communication when attempting to interpret natural language.

Natural Language Processing (NLP) is a branch of Artificial Intelligence that leverages machine learning to enable computers to interpret human language. By utilising datasets to create software that is capable of understanding the syntax, semantics, and context of conversations, NLP has become a critical component of modern technology. From home appliances to workplace tools, NLP is being used in a wide range of applications, and is becoming an increasingly essential part of our everyday lives.

Machine Learning (ML) utilises learning models in order to control its comprehension of human speech. By building upon a foundation of Machine Learning, the technology is able to teach itself new skills by analysing existing ones. While processing data, ML can draw from a variety of models, and is capable of responding to inquiries ranging from the common to the uncommon. This technology is able to adapt and learn over time, and is able to autonomously handle special cases without the need for rewriting the original code.

ML and NLP: How They Relate

At times, the relationship between Machine Language (ML) and Natural Language Processing (NLP) is not fully understood. While ML can be utilised in NLP technology, there are many interpretations of NLP that do not require Artificial Intelligence (AI) or ML to operate. One type of NLP technology is designed to extract only the most essential data and may potentially be based on AI-free systems.

On the contrary, more intricate applications of machine learning Natural Language Processing (NLP) may benefit from the implementation of Machine Learning (ML) models to better understand and interpret human speech. Additionally, ML models may also help to adjust to changes in human language use over time. Contrarily, NLP applications may be powered by unsupervised machine learning, supervised machine learning, both, or none, or even other systems.

Machine learning has a wide range of applications in natural language processing, allowing it to identify patterns in human speech, understand the meaning of the context, discern pertinent information from both written and verbal inputs, and learn new material independently. To facilitate a meaningful exchange with humans, the use of machine learning to recognise the context is essential for more intricate applications.

The use of various mathematical systems is integral to machine learning for natural language processing. This includes the ability to identify parts of speech, emotions, entities, and other textual characteristics. Supervised machine learning involves creating a model that can be applied to new collections of texts. On the other hand, unsupervised machine learning utilises a set of algorithms to analyse a large dataset and draw out meaningful information from it.

It is essential to comprehend the primary difference between supervised and unsupervised learning when working with Natural Language Processing (NLP) in Machine Learning. Incorporating both techniques into one system simplifies the process of optimising efficiency.

Given the sparsity of NLP text data, which can be composed of hundreds of dimensions such as phrases and words, a specialised approach to machine learning is necessary. As an example, the English language is known to contain approximately 170,000 words (as stated in the Oxford English Dictionary). Nevertheless, a tweet usually has only a few hundred characters.

Predicting natural language use with supervised machine learning

Supervised Machine Learning (ML) involves annotating text with examples of what the system should search for and how it should interpret it. This annotation is used to train a statistical model by providing examples of correctly and incorrectly labelled text for the system to learn from. Once the model has developed an understanding of the text it is analysing, it may be retrained with larger or more comprehensive datasets. As an example, supervised ML could be used to teach a model to read and utilise the star ratings given by critics who have reviewed a particular movie or television program.

For the model to achieve optimal performance, the data it is supplied with must be accurate and free from any irregularities. This is because supervised machine learning algorithms rely on receiving high-quality data in order to be effective. Once the model has had an adequate amount of training, it can be provided with unmarked data, at which point it can draw conclusions and make assessments based on what it has learnt from the labelled samples.

The utilisation of statistical models enables this specific variant of Natural Language Processing machine learning to develop a deeper understanding of the data. As it continues to learn, the accuracy of the analysis increases, allowing data scientists to provide it with more text for further examination. Nevertheless, due to its heavy reliance on statistical simulations, this machine learning use case may occasionally struggle to comprehend complex or unusual scenarios.

Data scientists employ a range of techniques to enable computers to learn, depending on the application, but some of the most widely used approaches are:

  • Categorization: It is essential to impart a wide range of information to the machine in order to enable it to gain an in-depth understanding of the context. By utilising this knowledge to create a model of how the text behaves, the machine is able to gain a more comprehensive comprehension of the background of the text.
  • Tokenization: Before processing the data, it is necessary to parse the text into distinct words, known as tokens, so that the computer can recognise and assign labels to the various topics discussed.
  • Classification: Using this method, we are able to determine which classification best fits the information provided in the text.
  • Analysing feelings: In this section, we seek to analyse the text data in order to determine the perceived mood of the message being conveyed by the author. Through careful examination of the content, we will determine whether the author’s tone is negative, neutral, or positive.
  • The tagging of parts of speech: You may draw parallels to diagramming English sentences here. But this time it’s for natural language processing in AI.
  • Recognising Proper Nouns: A data scientist then looks for significant items, such proper nouns, after giving the machine individual words.

Automatic natural language processing

In unsupervised machine learning, a model is trained without the need for any labels or annotations, which makes it challenging in comparison to supervised machine learning. However, it requires much less data and human effort to achieve the same results as supervised ML.

The three most frequent types of unsupervised machine learning systems are:

  • Calculating a Matrix’s factorization: This approach enables the system to identify underlying components in data matrices that have been chosen with precision; these factors can be determined by employing multiple techniques, all of which share some commonalities.
  • Clustering: A collection of papers that share similarities is generated by the system. The data pyramid is then used to sort data by significance and relevance.
  • What is latent semantic indexing (LSI)? An important aspect of this procedure is determining which terms or expressions are frequently encountered together in different circumstances. Engineers employ Latent Semantic Indexing (LSI) for search requests that are not an exact search phrase, and for searches that involve multiple facets.

This concept of contextual relevance is frequently discussed when discussing Search Engine Optimisation (SEO) and search engines in general. It is deployed by Google when suggesting search results, which may include related terms based on the context of the query.

Crucial Python NLP Library Packages

There are a plethora of libraries available for use in NLP applications, however the ones listed below are among the most ubiquitous.

NLT: Natural Language Toolkit (NLTK)

Python developers have access to one of the most powerful frameworks for dealing with human language data – the Natural Language Toolkit (NLTK). This framework contains various text-processing features such as sentence recognition, tokenization, lemmatization, stemming, parsing, chunking and part-of-speech (POS) tagging. Additionally, NLTK’s application programming interfaces (APIs) provide access to more than 50 corpora and lexical resources. This makes NLTK an invaluable tool for any Python developer looking to process data related to human language.


Due to its design for usage in industrial settings, spaCy is an open-source Python NLP software that enables the development of programs capable of processing large volumes of text. This makes it an ideal tool for data mining and NLP development. Additionally, it supports tokenization for over 49 languages, thanks to its word vectors and pre-trained statistical models.


TextBlob is a convenient and easy-to-use library providing access to a range of Natural Language Processing (NLP) processes. These processes include Part-of-Speech (POS) tagging, noun phrase extraction, sentiment analysis, classification, language translation, word inflection, parsing, n-grammes and WordNet incorporation. The results of using TextBlob are comparable to those obtained by applying NLP techniques to strings written in the Python programming language.


CoreNLP is a library written in the Java programming language, requiring that the device it is deployed on is capable of running Java. However, the library also provides interfaces for a number of popular programming languages, including Python. It incorporates a wide range of natural language processing (NLP) capabilities developed by Stanford, such as a named entity recognizer (NER), part-of-speech tagger, sentiment analyzer, bootstrapped pattern learner and coreference resolution system. Additionally, CoreNLP is also compatible with Arabic, Chinese, German, French, and Spanish.

Advances in natural language processing and deep learning

Deep Learning (DL) and Natural Language Processing (NLP) are two terms that are frequently encountered in conversations about Machine Learning and NLP applications. DL is a system which attempts to replicate the functioning of the human brain by implementing a large neural network. This technology is typically used to develop Machine Learning systems, resolve complex NLP situations, and handle continuously increasing datasets.

Deep learning is a subset of machine learning that utilises multiple layers of computational processing to acquire a more comprehensive understanding of the data being studied. By delving deeper into the data than traditional machine learning techniques, deep learning produces more accurate and extensive results that can be easily scaled. Consequently, deep learning has become a popular and effective tool for extracting meaningful insights from large datasets.

When faced with the need for learning and growth, Deep Learning (DL) does not disappoint in the same way that Machine Learning (ML) may. DL begins by studying the basics and gradually progresses to more intricate subjects. This makes it an ideal solution for Natural Language Processing (NLP) applications that require a deep understanding of the material.

In recent years, there has been a resurgence of interest in the field of Natural Language Processing (NLP) through the accessibility of Machine Learning (ML) and Deep Learning (DL) algorithms. In order to maximise the accuracy of NLP implementations, researchers have explored a wide range of DL methods, such as Autoencoders, Deep Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, and Limited Boltzmann Machines.

As we have observed, the integration of machine learning into Natural Language Processing (NLP) applications offers considerable advantages. The combination of NLP and machine learning allows us to tackle complex problems related to natural language, such as conversation generation, machine translation, sentiment analysis, question answering systems, chatbots and information retrieval systems. These technologies, as well as NLP, are rapidly evolving, so they are worth staying abreast of.

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