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Natural Language Processing NLP Applications and Techniques

Dr. Jagreet Kaur Gill | 22 June 2023

Natural Language Processing Applications and Techniques

What is Natural Language Processing?

Natural Language Processing involves the ability of machines to understand and derive meaning from human languages.

Machines can understand human language. It could be in the form of speech/text. It uses ML (Machine Learning) to meet the objective of Artificial Intelligence. The ultimate goal is to bridge how people communicate and what computers can understand.

If we mathematically represent it contains the following terms:

  • NLP: NLP (Natural Language Processing) is in charge of processes such as decisions and actions.
  • NLU: NLU (Natural Language Understanding) understands the meaning of the text.
  • NLG: NLG (Natural Language Generation) creates the human language text from the structured data that the system generates to answer.

A subset technique of Artificial Intelligence is used to narrow the communication gap between the Computer and Humans.

How many types are there?

There are three different levels of linguistic analysis-

  • Syntax - What part of the given text is grammatically right.
  • Semantics - What is the meaning of the given text?
  • Pragmatics - What is the purpose of the text?
It is a subset technique of Artificial Intelligence which is used to narrow the communication gap between the Computer and Human. Click to explore about, Evolution and Future of Natural Language Processing
NLP deal with different aspects of language such as:
  • Phonology - It is a systematic organization of sounds in language.
  • Morphology - It is a study of words formation and their relationship with each other.
Approaches of NLP for understanding semantic analysis.
  • Distributional - It employs large-scale statistical tactics of Machine Learning and Deep Learning.
  • Frame-Based - The sentences which are syntactically different but semantically same are represented inside data structure (frame) for the stereotyped situation.
  • Theoretical - This approach builds on the idea that sentences refer to the real world (the sky is blue) and parts of the sentence can be combined to represent whole meaning.
  • Interactive Learning - It involves a pragmatic approach and the user is responsible for teaching the computer to learn the language step by step in an interactive learning environment.
The real success of it lies in the fact that humans deceive into believing that they are talking to humans instead of computers.

Importance of its Applications

With NLP, it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time. Due to the presence of significant data (text) around, why not we use the computers untiring willingness and ability to run several algorithms to perform tasks in no time. These tasks include other NLP applications like Automatic Summarization (to generate a summary of given text) and Machine Translation (translation of one language into another

Techniques for Natural Language Processing?

In case the text is composed of speech, the speech-to-text conversion is performed. The mechanism of Natural Language Processing involves two processes -
  • Natural Language Understanding
  • Natural Language Generation

Natural Language Understanding

NLU or Natural Language Understanding tries to understand the meaning of the given text. The nature and structure of each word inside text must be known for NLU. For understanding structure, NLU attempting to resolve following ambiguity present in natural language -
  • Lexical Ambiguity - Words have multiple meanings
  • Syntactic Ambiguity - Sentence is having multiple parse trees.
  • Semantic Ambiguity - Sentence having multiple meanings
  • Anaphoric Ambiguity - Phrase or word which is previously mentioned but has a different meaning.

Next, the sense of each word is understood by using lexicons (vocabulary) and set of grammatical rules. However, certain different words are having similar meaning (synonyms) and words having more than one meaning (polysemy).

Natural Language Generation

It is the process of automatically producing text from structured data in a readable format with meaningful phrases and sentences. The problem of natural language generation is hard to deal with. It is a subset of NLP Natural language generation divided into three proposed stages -

  • Text Planning - Ordering of the primary content in structured data is done.
  • Sentence Planning - The sentences are combined with structured data to represent the flow of information.
  • Realization - Grammatically correct sentences are produced finally to represent text.

Text Mining vs Natural Language Processing

nlp-vs-text-miningIt is responsible for understanding the meaning and structure of a given text. Text Mining or Text Analytics is a process of extracting hidden information inside text data through pattern recognition. It is used to understand the meaning (semantics) of given text data, while text mining is used to understand the structure (syntax) of given text data. As an example - I found my wallet near the bank. The task of it is to figure out at the end that ‘bank’ refers to a financial institute or ‘river bank.'

What is Big Data?

According to the Author Dr. Kirk Borne, Principal Data Scientist, Big Data Definition is described as big data is everything, quantified, and tracked.

Big Data For Natural Language Processing

Today around 80 % of total data is available in the raw form. Big Data comes from information stored in big organizations as well as enterprises. Examples include information about employees, company purchase, sale records, business transactions, the previous record of organizations, social media, etc. Though human uses language, which is ambiguous and unstructured to be interpreted by computers, yet with the help of NLP, this large unstructured data can be harnessed for evolving patterns inside data to know better the information contained in data. It can solve significant problems of the business world by using Big Data. Be it any business of retail, healthcare, business, financial institutions.

Deep Learning For NLP Applications


  • It uses a rule-based approach that represents Words as ‘One-Hot’ encoded vectors.
  • The traditional method focuses on syntactic representation instead of semantic representation.
  • Bag of words - classification model is unable to distinguish certain contexts.

3 Capability Levels of Deep Learning Intelligence

  • Expressibility - This quality describes how well a machine can approximate universal functions.
  • Trainability - How well and quickly a Deep Learning system can learn its problem.
  • Generalizability - How well the machine can perform predictions on data that it has not been trained.
There are of course other capabilities that also need to be considered in Deep Learning such as Interpretability, modularity, transferability, latency, adversarial stability, and security. But these are the main ones.

Applications of Deep Learning in NLP

Deep Learning Algorithms NLP Usage

Neural Network - NN (feed)


- Part-of-speech Tagging
- Tokenization
- Named Entity Recognition
- Intent Extraction

Recurrent Neural Networks -(RNN)


- Machine Translation
- Question Answering System
- Image Captioning

Recursive Neural Networks


- Parsing sentences
- Sentiment Analysis
- Paraphrase detection
- Relation Classification
- Object detection

Convolutional Neural Network -(CNN)


- Sentence/ Text classification
- Relation extraction and classification
- Spam detection
- Categorization of search queries
- Semantic relation extraction

NLP for  Log Analysis & Log Mining?

Its techniques are widely used in Log Analysis and Log Mining. The different techniques such as tokenization, stemming, lemmatization, parsing, etc. are used to convert log messages into structured form. Once logs are available in the well-documented form, log analysis, and log mining is performed to extract useful information and knowledge is discovered from the information. The example in case of an error log caused due to server failure.

A subset of Artificial Intelligence. It processes large amounts of human language data. It is an end to end process between the system and humans. Read Differences Between NLP, NLU, And NLG?

What are the best Techniques of Natural Language Processing?

Different methods used for performing log analysis are described below

Pattern Recognition

It is one such technique which involves comparing log messages with messages stored in pattern book to filter out messages.

Text Normalization

Normalization of log messages is done to convert different messages into the same format. This is done when different log messages have different terminology, but the same interpretation is coming from various sources like applications or operating systems.

Automated Text Classification & Tagging

Classification & Tagging of different log messages involves ordering of messages and tagging them with the various keywords for later analysis.

Artificial Ignorance

It is a kind of technique using Machine Learning Algorithms to discard uninteresting log messages. It is also used to detect an Anomaly in the ordinary working of systems.
Focus on helping computers to understand the way that humans speak and write. Read about Natural Processing Language in AI

Diving into Natural Language Processing Applications

It is a complex field and is the intersection of Artificial Intelligence, computational linguistics, and computer science.

Getting started with Natural Language Processing

The user needs to import a file containing text written. Then the user should perform the following steps.
Technique Example Output
Sentence Segmentation Mark met the president. He said:”Hi! What’s up -Alex?” - Sentence 1 - Mark met the president.
- Sentence 2 - He said: ”Hi! What’s up - Alex?”
Tokenization My phone tries to ‘charging’ from ‘discharging’ state. - [My] [phone] [tries] [to] [‘] [charging] [‘][from] [‘][discharging] [‘] [state][.]
Stemming/Lemmatization Drinking, Drank, Drunk - Drink
Part-of-Speech tagging If you build it he will come. - IN - prepositions and subordinating conjunctions.
- PRP - Personal Pronoun
- VBP - Verb Noun 3rd person singular present form.
- PRP- Personal pronoun
- MD - Modal Verbs
- VB - Verb base form
Parsing Mark and Joe went into a bar. - (S(NP(NP Mark) and (NP(Joe))
- (VP(went (PP into (NP a bar))))
Named Entity Recognition Let’s meet Alice at 6 am in India. - Let’s meet Alice at 6 am in India
- Person Time Location
Coreference resolution Mark went into the mall. He thought it was a shopping mall. - Mark went into the mall. He thought it was a shopping mall.
  • Sentence segmentation - It identifies sentence boundaries in the given text, i.e., where one sentence ends and where another sentence begins. Sentences are often marked ended with punctuation mark ‘.’
  • Tokenization - It identifies different words, numbers, and other punctuation symbols.
  • Stemming - It strips the ending of words like ‘eating’ is reduced to ‘eat.’
  • Part of speech (POS) tagging - It assigns each word in a sentence its own part-of-speech tag such as designating word as noun or adverb.
  • Parsing - It involves dividing given text into different categories. To answer a question like this part of sentence modify another part of the sentence.
  • Named Entity Recognition - It identifies entities such as persons, location and time within the documents.
  • Co-Reference resolution - It is about defining the relationship of given the word in a sentence with a previous and the next sentence.
Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs. Click here for our Google Cloud Natural Language Solutions

What are the key Application of Natural Language Processing?

Apart from use in Big Data, Log Mining, and Log Analysis, it has other significant application areas. Although the term ‘NLP’ is not as popular as ‘big data’ ‘machine learning’ but we are using it every day.

Automatic Text Summarizer

Given the input text, the task is to write a summary of text discarding irrelevant points.

Sentiment-based Text Analysis

It is done on the given text to predict the subject of the text, eg, whether the text conveys judgment, opinion or reviews, etc

Text Classification

It is performed to categorize different journals, news stories according to their domain. Multi-document classification is also possible. A famous example of text classification is spam detection in emails. Based on the style of writing in the journal, its attribute can be used to detect its author's name.

Information Extraction

Information extraction is something which proposes email program add events to the calendar automatically.

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Text Analytics Solutions

Text Analytics or Text Mining refers to the automatic extraction of high-value information from text. The extraction involves structuring the input text, discovering patterns in the structured data and interpreting the results. Text Mining process involves Machine Learning, Statistics, Data Mining, and Computational Linguistics. Sentiment Analysis Using Machine Learning, NLP, and Deep Learning At XenonStack, we process and analyze textual content and provide valuable insights by transforming the raw data into structured, usable information. XenonStack's Text Analytics Solutions offers Part-of-Speech (PoS) tagging, Clustering, Classification, Information Extraction, Sentiment Analysis and more.

Sentiment Analysis Using Machine Learning, NLP, and Deep Learning

Sentiment Analysis helps to apprehend people's reaction to situations. Sentiment Analysis is used to predict person's emotions like angry, happy, sad, disgust etc. XenonStack offers Sentiment Analysis and Intent Analytics using Machine Learning, Natural Language Processing, Deep Learning, Supervised Learning Algorithms, Keras with Tensorflow. Enhance the customer experience through Sentiment Analysis in Business.

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