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Natural Language Processing: 11 Key NLP Techniques

  • Hrithik Saini
  • Jul 07, 2022
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Machines that can emulate the functionality and capacities of the human psyche have long been a goal of artificial intelligence. Language is regarded as among the most significant technological discoveries that have influenced humankind's growth. 

 

As a result, it's no mystery that there is still a lot of effort being performed in the area of artificial intelligence to incorporate speech into the field of Natural Language Processing (NLP). Alexa and Siri, for example, are examples of the work that has been done.

 

You may remain on top of your NLP practices and optimize your organization by deconstructing them in the methods we'll explain in this post. You can explore at your own pace or go directly to natural language processing techniques.

 

 

What is Natural Language Processing?

 

Natural Language Processing (NLP) is an area of Artificial Intelligence (AI) that empowers machinery to understand human conversation. NLP harnesses the capabilities of language studies and computer programming to investigate the laws and grammatical structure and construct intelligent tools and platforms for interpreting, analyzing, and obtaining reading comprehension in the process and voice (classic machine learning and NLP techniques).

 

Artificial intelligence (AI) technologies such as artificial intelligence and deep learning are promising alternatives to a world of limitless potential. To understand the meaning of massive data, machine learning is rapidly being employed in data analytics. 

 

It's also used to create chatbots that imitate human-to-human interactions with clients. These forward-thinking machine learning algorithms, however, would not be conceivable without the innovation of Natural Language Processing (NLP).

 

Also Read | Major Uses of NLP

 

 

Why is Natural Language Processing Important?

 

Machine translation is the most extensively used NLP application, which aids in overcoming language barriers. As the size of the dataset on the internet grows, the demand to access and analyze it grows as well.

 

Machine translation can be used to transform data from one language to another. The NLP approach assists the computer in comprehending the meaning of phrases, which increases machine translation efficacy.

 

For sentiment analysis, NLP approaches are particularly useful. It aids with the recognition of sentiment in a variety of online postings and comments. From online reviews, businesses use NLP approaches to learn approximately their consumers' opinions regarding their products or services.

 

Non-developers may now cooperate with computer systems and extract useful data using modern NLP techniques. The fundamental alternatives for the input terms may be discovered and coordinated with the proper replies using NLP, which aids users who are unfamiliar with the computer system's terminology.

 

Other common uses of NLP include spam filtering, word sense disambiguation, text categorization, knowledge discovery, question answering, networking website broadcasts, voice commands, and way of speaking.

 

 

Top 11 NLP Techniques You Must Know


Here are the top 11 Natural Language Processing Techniques you must know about.

Top 11 NLP Techniques


Let's look at the 11 best NLP techniques that are being used behind the settings of some of the most amazing natural language processing apps.

 

  1. Tokenization

 

During any NLP workflow, tokenization is the important first step. It has a significant impact on the remainder of your pipeline. Unstructured data and natural language text are broken down into snippets of data which can be regarded as separate parts using a tokenizer. The token frequencies in a text can be utilized to create a matrix that represents the message.

 

An unstructured string (text document) is instantly transformed into a statistical analysis structure suited for machine learning. Algorithms should only be used to guide helpful activities and reactions by a computer. 

 

They might also be utilized as features in a machine learning pipeline to trigger more complicated choices or behavior. There are a variety of tokenization strategies available, with Porter's Algorithm being one of the most well-known.

 

  1. Stop Words Removal

 

Stop words are words that are often extracted just before the natural language is processed. These are the most often used keywords in any speech, and they don't add anything to the content. Stop words in English include "the," "a," "an," "so," and "what."

 

In any natural language, there are plenty of stop words. We eliminate the uninformed voters from the text by deleting these terms, allowing us to focus more on the essential information. In other words, we may argue that removing such phrases has no detrimental impact on the model we are training for our job.

 

Stop words may be removed in a variety of ways utilizing libraries like Genism, SpaCy, and NLTK. To study the stop word elimination NLP approach, utilize the SpaCy package. For most languages, SpaCy shows a collection of stop words.

 

Also Read | NLP Techniques for Extracting Information

 

  1. Topic Modeling

 

Topic modeling is an unsupervised machine learning technique that scans a set of texts for phrase and word structures, then autonomously clusters word groups and related statements that properly represent the overall dataset.

 

This technique of natural language processing is characterized as 'unsupervised' machine learning since it does not require a prior set of tags or development in order which has been previously categorized by individuals.

 

However, this must not be confused with various topic categorization models, which are supervised machine learning approaches. It's important to understand what a set of papers is about before diving into them. These topics are explicitly classified in the data so that a topic classifier may learn and forecast on its own.

 

  1. Sentimental Analysis

 

Sentiment analysis is an introspective approach that determines the significant experiences of interactions by combining statistics, natural language processing, and machine learning.

 

Customers' communications, contact center encounters, online reviews, social media postings, and other information are all evaluated using sentiment analysis. Changes in opinions regarding organizations, goods, or services, as well as particular elements of such items or services, may be tracked using sentiment analysis.

 

Sentiment analysis may also be performed for brand management, to assist a firm to comprehend how different groups of its existing customers perceive its production and better reach targeted advertisements to them.

 

 

  1. Named Entity Recognition

 

There are special words that indicate unique elements that are much more meaningful and also have a multidimensional view in any written composition. These are known as named entities, which are words that reflect real-world items such as persons, locations, organizations, and so on, and are frequently represented by proper names.

 

A basic method may be to seek them in text documents' linguistic expressions. Named entity recognition (NER), also described as entity chunking/extraction, is a common approach for identifying and segmenting named entities, as well as classifying and categorizing them into several predetermined classes.

 

Also Read | NLP Libraries with Python

 

 

  1. Stemming and Lemmatization

 

Stemming is the way of decreasing conjugated languages to their grammatical structures, base, or part of an organism, which is usually the written form. Stemming, for example, essentially removes all suffixes. As a result, the term "playing" turns "play" after a process of stemming, just as "asked" turns "ask."

 

Lemmatization is a term that refers to actions taken with the right use of terminology and properties of language of words, with both the goal of removing only affixation ends and returning the baseline or vocabulary form of a word, known as the lemma. Lemmatization, in basic terms, is the process of lowering the grammatical structure after recognizing the part of speech or situation of the term in the document.

 

 

  1. Text Summarization in NLP

 

Would you just start to read each headline story whenever you open a news channel? Most likely not. We usually skim the headlines and then read the full story if we're fascinated. Short, useful news summaries may now be found in publications, content aggregator applications, research sites, and other places.

 

As news arrives from a variety of sources throughout the world, it is feasible to produce the descriptions autonomously.

 

Text summarization is the process of obtaining these summaries from a large text without compromising important information. It is critical that the summary be fluid, comprehensive, and depicts the importance.

 

In reality, text summarization algorithms are used by Google News, in shorts, and a variety of other content aggregator applications.

 

 

  1. Keyword Extraction 

 

Assume you want to look for a huge wide range of product reviews on the web. Keyword extraction may be used to look through several pieces of data and locate the phrases that best characterize every assessment. You'll be able to witness which themes are producing the greatest debate among your customers, and streamlining the process can save a huge amount of time for your employees.

 

The process of extracting important information from a sequence of paragraphs or papers is known as keyword extraction. The automated approach to identifying the most important words and expressions from text input is known as keyword extraction. It's a text summarization technique that includes identifying the most essential words or phrases from a page autonomously.

 

 

  1. TF-IDF (Term Frequency-Inverse Document Frequency)

 

The TF-IDF (term frequency-inverse document frequency) confidence interval examines the relevance of a term to a document in a document corpus.

 

This is accomplished by combining different benchmarks: the multitude of times a word occurs in a document as well as the phrase's inverse document prevalence over a collection of documents.

 

It has a variety of applications, including automatic content analysis and evaluation of keywords in machine learning applications for Natural Language Processing (NLP).

 

 

  1. Machine Translation

 

Last but not least, Machine Translation is a significant NLP tool. The techniques that belong underneath the Machine Translation framework are used to analyze as well as produce language. Complex machine translation technologies are used by top corporations.

 

They are extremely important in modern trade. These technologies have helped individuals all across the entire globe break down communication barriers, allowing them to visit legitimate sites and engage with others who speak other languages. The Machine Translation sector generated $40 billion in sales last year.

 

Neural Machine Translation (NMT) is an indispensable component of machine translation. Machine translation methods that use neural techniques use artificial neural networks to estimate probabilities via word sequence analysis, combining several phrases into a single integrative framework.

 

Also Read | Difference b/w NLP & NLU

 

 

  1. Word Embedding

 

We understand that machine learning and deep learning techniques only accept mathematical input, then how can we transform a block of text into integers that could be fed to these modeling techniques? When developing a model using text data, whether it's for classification or regression, it's a must to convert it to a numerical representation. 

 

The solution is straightforward: use the word embedding method to represent text data. This NLP approach allows you to express words that have comparable connotations in a similar way.

 

The mathematical interpretations of lexical items are known as word embeddings, sometimes referred to as vectors. These visualizations are learnt in such a way that vectors for phrases with alternate meanings are relatively near to one another. Individual variables are represented in n-dimensional feature space as real-valued trajectories or waypoints.

 

 

Conclusion

 

Overall, NLP is still in its infancy. Hundreds of important vocabulary subtleties and problems must be handled. Experts expect to solve independent machine learning problems at an accelerated rate as a result of large expenditures in associated domains such as mortal classification techniques. These advanced methods are designed to make our lives considerably easier.

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