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10 Natural Language Processing Methods

  • Ayush Singh Rawat
  • Jan 23, 2022
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Natural language processing (NLP) is the capacity of computer software to interpret spoken and written human language, often known as natural language. It's a part of AI (artificial intelligence).


NLP has origins in linguistics and has been around for more than 50 years. It has a wide range of practical uses, including medical research, search engines, and corporate intelligence.


NLP is a text analysis technique that allows robots to interpret human speech. Automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, connection extraction, stemming, and other real-world applications are made possible by this human-computer interaction. 


Text mining, machine translation, and automated question answering are all examples of how NLP is employed.


NLP is regarded as a challenging subject in computer science. Human language is rarely exact or simple to understand. To comprehend human language, one must comprehend not just the words, but also the concepts and how they are related to producing meaning. 


Despite the fact that language is one of the easiest things for the human mind to acquire, its ambiguity makes NLP a challenging subject for computers to grasp.


Top 10 NLP methods

  1. Stemming and Lemmatization


Stemming or lemmatization is one of the most essential NLP methods in the preprocessing pipeline. 


When searching for things on Amazon, for example, assume we want to display products not just for the precise term we placed in the search field, but also for other alternative variants of the word we typed. 


If we type "shirts" into the search box, it's extremely probable that we'll want to view product results that have the form "shirt." Similar words in the English language seem different depending on the tense and where they are used in a phrase.


Words like go, going, and went, for example, are all the same thing but are used differently depending on the context of the phrase. The stemming or lemmatization NLP approach seeks to produce root words from these word variants. 


Stemming is a rudimentary heuristic process that attempts to achieve the aforementioned aim by slicing off the ends of words, which may or may not result in a meaningful term at the end. 


On the other hand, lemmatization is a more advanced approach that seeks to accomplish things correctly through the use of a vocabulary and morphological study of words. It returns the basic or dictionary form of a word termed a lemma by eliminating the inflectional ends.


  1. Stop Words Removal


Stop words removal is a preprocessing operation that follows stemming or lemmatization. Many words in any language serve only as fillers and have no significance. 


These are mostly words that are used to link sentences (conjunctions- "because", "and"," since") or to illustrate a word's relationship to other words (prepositions- "under", "above", "in", "at"). 


These words make up the majority of human speech but aren't particularly effective for building an NLP model. Stop word removal, on the other hand, is not a certain NLP strategy to use for every model because it relies on the goal.


When undertaking text classification, for example, deleting stop words from the text helps the model focus on terms that determine the meaning of the text in the dataset (genre classification, spam filtering, auto tag creation). 


Stop words removal may not be necessary for tasks such as text summary and machine translation. Stop words may be removed in a variety of ways utilising libraries like Genism, SpaCy, and NLTK


To learn about the stop words removal NLP approach, we'll utilize the SpaCy package. For most languages, SpaCy includes a list of stop words.


  1. Imagery training


Imagery training, often known as mental rehearsal, is a classic visualization-based neuro-linguistic programming approach. Because it is simple and linear, it is a good exercise for beginners. 


The goal is to see oneself effectively completing an activity, whether that task is nailing a presentation or mastering your golf putt. Consider your demeanour: assured, resolute, and at ease.


Feel your self-assurance and the energy that surrounds you. Provide as much information as possible. This type of NLP method is critical for instilling complete confidence in yourself and your talents.


  1. Keywords extraction


Keyword extraction, often known as keyword identification or keyword analysis, is a natural language processing (NLP) approach for text analysis.


The main goal of this approach is to automatically extract the most common words and phrases from a text's body. It is frequently used as a first step in summarising a text's primary concepts and delivering the text's essential themes.


The strength of machine learning and artificial intelligence is hidden in the backend of keyword extraction techniques. They are used to extract and simplify a given text so that the computer can interpret it.


An algorithm may be customised and utilised in a variety of contexts, ranging from academic material to colloquial text in social media posts.


In today's environment, keyword extraction has a variety of uses, including social media monitoring, customer service/feedback, product research, and SEO. (Here)


  1. Topic Modelling


Keyword extraction techniques may be used to reduce a big body of text to a few primary keywords and concepts. You may probably deduce the text's major point from this.


Subject modelling—topic modelling based on unsupervised machine learning that does not require labelled data for training—is another, more complex approach for determining a text's topic.


Correlated Subject Model, Latent Dirichlet Allocation, and Latent Sentiment Analysis are some of the techniques that may be used to model a text topic. The Latent Dirichlet method is the most often used method. 


This method examines the text, breaking it down into words and statements, and then extracting various subjects from these words and assertions. All you have to do is provide the algorithm with a body of text, and it will handle the rest.


  1. Named Entity Recognition


Named Entity Recognition, or NER (because we techies love acronyms), is a Natural Language Processing approach that tags and extracts 'named identities' from the text for subsequent analysis.


NER is related to sentiment analysis, as shown in the sample below. NER, on the other hand, simply tags the IDs, whether they be organisation names, persons, proper nouns, locations, or anything else, and keeps track of how many times they appear in a dataset.


The number of times an identifier (a term that refers to a certain object) appears in customer feedback might suggest the need to address a particular issue. 


It can show a preference for particular types of items in reviews and searches, allowing you to adapt each customer journey to the unique user and thereby improve their customer experience. Your input and the content teams' ideas are the only boundaries to NER's application.


  1. Text Summary


This is a lot of fun. Text summarising is the use of natural language processing to break down the jargon, whether scientific, medical, technical or otherwise, into its most basic concepts in order to make it more intelligible.


Our languages are complex, so this may seem frightening. Text summary software, on the other hand, can swiftly synthesise sophisticated language into a compact result by using fundamental noun-verb linking algorithms.


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


TF-IDF computes "weights" that describe how important a word is to a document in a collection of documents, unlike the CountVectorizer (aka corpus). 


The TF-IDF value rises in direct proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that include the term. 


To put it another way, the greater the TF-IDF score, the rarer, distinctive, or valuable the phrase is, and vice versa. It has applications in information retrieval, such as search engines, which strive to offer the most relevant results to what you're looking for.


  1. Bag of Words


The Bag of Words (BoW) model is a representation that converts text into vectors of fixed length. This allows us to convert text to numbers, which we can then employ in machine learning models


The model is simply concerned with the frequency of words in the text and is unconcerned with their arrangement. It has applications in NLP, document categorization, and information retrieval from documents. (Here)


  1. Aspect Mining


Aspect mining is a technique for identifying the many features of a text. It pulls comprehensive information from the text when used in combination with sentiment analysis. Part-of-speech tagging is one of the simplest ways of aspect mining.


When aspect mining and sentiment analysis are applied to the example text, the result reflects the text's whole intent:


Aspects & Sentiments:


  • Customer service – negative

  • Call centre – negative

  • Agent – negative

  • Pricing/Premium – positive 





The desire of humans for computers to comprehend and communicate with them in spoken languages is as ancient as computers themselves. This concept is no longer simply a concept, thanks to rapid technological advancements and machine learning algorithms. It is a fact that we can see and feel in our everyday lives. This concept lies at the heart of natural language processing.


Natural language processing is one of the hottest subjects and areas nowadays. Companies and academic institutions are racing to develop computer systems that fully comprehend and utilise human languages. Since its inception in the 1960s, virtual agents and translators have quickly advanced.

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