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Natural language processing enables computers to speak with humans in their native language while also automating other language-related processes. NLP, for example, enables computers to read text, hear voice, analyse it, gauge sentiment, and identify which bits are significant.

NLP is becoming more commonplace as language technology is applied to industries as disparate as shopping (for example, in customer service chatbots) and medicine (interpreting or summarising electronic health records). NLP is used by conversational agents such as Amazon's Alexa and Apple's Siri to listen to user inquiries and discover answers. The most powerful such agents, such as GPT-3, which was recently made available for commercial use, can generate sophisticated text on a wide range of topics and power chatbots capable of holding logical conversations. NLP is used by Google to improve search engine results, and by social networks such as Facebook to detect and censor hate speech.

A combination of NLU and NLG gives an NLP system.

  • NLP (Natural Language Processing): It understands the text's meaning.

  • NLU (Natural Language Understanding): It handles entire processes such as decisions and actions.

  • NLG (Natural Language Generation): It creates human language text from structured data generated by the system in order to respond.

NLP and NLU are important words to use when designing a machine that can readily interpret human language, regardless of if it has any defects. There is a subtle difference between the terms that developers must understand if they want to build a machine that can interact with humans by providing them with a human-like environment, because using the right technique at the right time is critical to success in systems designed for Natural Language operations.

Correlation between NLP and NLU :


It is motivated by a hypothesis. It discusses syntactic structure and the goal of linguistic analysis. It is claimed to distinguish grammatical sentences from non-grammatical sentences of language in order to check the grammatical structure of the sequence. Syntactic analysis can be applied to a variety of processes. There are several strategies for aligning and grouping words in order to check grammatical rules:


  • Lemmatization :It simplifies analysis by reducing inflected versions of words and consolidating them into a single form.

  • Stemming : It reduces inflected words by cutting words to their root form.

  • Morphological segmentation:It split words into morphemes.

  • Word segmentation:It divides a continuous written text into distinct meaningful units.

  • Parsing:It analyses words or sentences by underlying grammar.

  • Part-of-speech tagging:This analyses and identifies parts of speech for each word.

  • Sentence breaking:It detects and places sentence boundaries in continuous text.

The validation of sentences or texts is not necessarily correlated by syntactic analysis. Grammar, whether right or faulty, is insufficient for this purpose. Other considerations must also be addressed. Another consideration is semantic analysis. It is used to interpret what words mean.

We have some techniques of semantic analysis:

  1. Named entity recognition (NER):It identifies and classifies text into predefined groups.

  2. Word sense disambiguation:It identifies the sense of words used in sentences. It gives meaning to a word based on the context.

  3. Natural language generation: It converts structured data into language.

There is one more critical aspect of semantics and syntactic analysis. It is known as Pragmatic analysis. It aids comprehension of the text's intent or goal. Sentiment analysis aids in achieving this goal.

What is NLP used for ?

NLP is used for a wide range of language-related tasks, such as answering inquiries, classifying content, and chatting with users.Here are few  jobs that NLP can handle.:

Sentiment Analysis :The practise of categorising the emotional purpose of text is known as sentiment analysis. In general, a sentiment classification model takes a piece of text as input and returns the likelihood that the sentiment expressed is positive, negative, or neutral. This probability is typically based on hand-generated features, word n-grams, TF-IDF features, or deep learning models that capture sequential long- and short-term dependencies. Sentiment analysis is used to categorise consumer evaluations on numerous online platforms, as well as for specialised applications such as detecting indicators of mental illness in online comments.

Toxicity classification is a subset of sentiment analysis in which the goal is to identify specific categories such as threats, insults, obscenities, and hatred towards certain identities as well as hostile intent. Text is fed into such a model, and the output is typically the probability of each kind of toxicity. Toxicity classification algorithms can be used to manage and improve online dialogues by silencing objectionable remarks, detecting hate speech, and detecting defamation in documents.

Machine translation is the automated translation of different languages. Text in a defined source language is fed into such a model, and the output is text in a specified target language. Google Translate is probably the most well-known mainstream application. These models are used to increase communication between users on social media networks like Facebook and Skype. Effective machine translation systems can distinguish between words with similar meanings. Some systems also do language identification, which is the classification of text as being in one or more languages.

Spam detection is a common binary classification problem in NLP, with the goal of classifying emails as spam or not. Spam detectors use an email content as input, along with different subtexts such as the title and sender's name. They want to know how likely the email is to be spam. Such models are used by email providers such as Gmail to give a better user experience by recognizing unsolicited and undesirable emails and moving them to a designated spam folder.

 Text generation, often known as natural language generation (NLG), generates text that resembles human-written text. Such models can be fine-tuned to generate text in a variety of genres and formats, such as tweets, blogs, and even computer code. Markov processes, LSTMs, BERT, GPT-2, LaMDA, and other techniques were used to generate text. It's very helpful for autocomplete and chatbots.

Autocomplete guesses the next word, and autocomplete systems of increasing sophistication are utilized in chat apps such as WhatsApp. Google's autocomplete feature predicts search terms. GPT-2 is a well-known autocomplete model that has been used to produce essays, song lyrics, and much more.

Chatbots often provide one side of a conversation while a human conversationalist provides the other.

Database query:  We have a database of questions and answers, and we want a user to query it using natural language.

Conversation generation: These chatbots may mimic human-to-human dialogue. Some people can hold wide-ranging talks. Google's LaMDA, for example, generated such human-like responses to questions that one of its engineers was certain it had feelings.

NLP is a fast-growing study subject in AI, with applications such as translation, summarization, text production, and sentiment analysis. Businesses utilize NLP to fuel an increasing number of applications, both internal and customer-facing, such as detecting insurance fraud, evaluating customer sentiment, and optimising aircraft maintenance.

Aspiring NLP practitioners can start by learning fundamental AI skills such as basic mathematics, Python coding, and employing algorithms such as decision trees, Naive Bayes, and logistic regression. Online classes might assist you in laying the groundwork.