Definition and Concept of AI in natural language:

Artificial Intelligence is a branch of computer science that puts efforts into creating machines and systems that work like humans. AI is utilized in complex problem-solving techniques such as navigation apps, pattern recognition commonly used for facial recognition systems, and understanding spoken languages such as those used in SIRI and Google Assistant. Moreover, active learning is used in the recommendation system of Netflix and other social media apps, and decision-making is used in autonomous vehicles. Artificial Intelligence leverages many methods and strategies, including Machine Learning, Neural networks, Deep learning, and Natural language processing. These allow computers to examine information, adjust to new inputs, and execute tasks that are almost identical to those of humans.

Artificial intelligence in natural language concerns various essential tasks. Natural language is one of the main concepts of AI because it deals with the machine’s ability to understand the meaning behind words and sentences. It tells us the context in which they are used, with the understanding of literal meaning, the intent of the writer, and the emotions conveyed as well. For example: when we ask from chatgpt “What is AI?”. NLU helps the system understand that you’re requesting a specific type of question and its focus should be on the Artificial Intelligence(AI) and it will not give content unrelated to it.

Another concept is Natural language generation (NLG). It produces human-like text based on the received data.NLG is responsible for creating logical and dependently appropriate responses generating reports from data, and composing writing. Machine translation is also a key concept of AI in Natural Language. This involves the automatic translation of speech or text from one language to another, text that requires deep knowledge of grammar, morphological terms, and contextual differences between languages.

Speech recognition is also a vital step of AI in Natural language. Speech recognition converts the spoken language into text form, which allows machines to understand the spoken language and respond to the commands. These concepts of AI in natural language making machines better is a key component of natural language processing and its goal is to understand and interact with human language.

How NLU Works?

As discussed earlier, NLU helps to understand the context, meaning, and intent of the text. NLU is essential for making human interactions more natural and effective.NLU works through different techniques that allow AI systems to analyze and interpret text or speech in a way that imitates human understanding. Different steps of NLU working which are given below.

Text preprocessing

In text preprocessing there are further three steps. First is tokenization, stop word removal and last one is Lemmatization. Tokenization is breaking down the input into small patches called tokens. It may be words, sentences, or phrases depending on the complexity of the project. For example: input is “I Love NLP” tokenization would be [‘I’ ‘Love’ ‘NLP’]. Stop word removal removes the non-significant words such as “a”, “an”, “the”, and “and” to focus on more important words. In Lemmatization words are reduced to their base form. For example: running will be reduced to run. This helps in treating different forms of a word as a single entity.

Syntax Analysis

In Syntax analysis, there are further two steps. The first is part of speech tagging, second one is Dependency parsing. In part of speech, Each token is tagged with its corresponding part of speech. For example: “I Love NLP” like  “I” is tagged as a “pronoun”, " Love” is tagged as a “verb” and “NLP” is tagged as a “noun”. Dependency parsing involves determining the relationship between words and analyzing the grammatical structure. It helps in comprehending the subject sentence, verb, and object which is a crucial step to determine the grammar steps. For example: “I Love NLP” like “I” is the subject, “Love ” is the verb, and “NLP” is the object.

Semantic analysis

In Semantic analysis, there are two steps. The first one is Named Entity Recognition (NER) and the second one is Word sense Disambiguation. NER identifies the entities in the text from predefined categories such as names of people, locations, and places. For example “Youtube is a very famous app”  “YouTube” would be identified as an organisation. Word sense Disambiguation uses context to determine the true meaning of the word in the given sentence because one word might have a different meaning. For example: the word “ sea” has a different meaning. NLU determines the correct meaning according to the sentence.

Contextual understanding 

In contextual understanding, there are further two steps. The first one is contextual embedding and the second one is sentiment analysis. Contextual embedding captures the meaning of the words based on the surrounding text, allowing the AI to understand these words in different contexts. For example: ‘Bank’  like “ I went to the bank to deposit money” and  “ The river bank was flooded” In sentiment analysis determines the sentiment of the text used in input. For example: “ I’m excited for a new Nasheed”  would be analyzed as a positive sentiment.

Intent Recognition

Intent recognition identifies the user’s intent behind the text. For example “  I Want to Go to England”  the intent is to make travel. This is a critical application in chatbots and virtual assistance.it is far more important in customer services where quick and accurate intent is required for faster resolution and higher customer satisfaction. Intent recognition is essential for making interactions with AI more efficient and accurate.

Output Generation

In Output generation, the NLU system generates an output or takes appropriate actions based on the intent of the user’s input. The accuracy and relevance of output generation are important to the user experience. If the NLU system accurately interprets the user’s input but fails to generate an accurate response, then interaction can become frustrating.NLU plays an important role in checking whether the output is relevant or not.

Customer support:

NLU allows chatbots and virtual assistants to understand customer questions and provide accurate, context-aware feedback. Whenever a customer asks a query from chatbots intent recognition is used for determining the goal and purpose behind a customer’s query. For example: A customer typing  “ I want pizza” will be recognized as a customer who wants a pizza item from the food point. In Entity recognition, it identifies key information within a query such as product name, Location, Time, date, etc. For example: “Track my order from last month” Two main elements are included in this statement “order” and “last month”.Chatbots are also used for maintaining Context across multiple interactions. Workoverflow of NLU in customer support is given below:

1. Query input

2. Preprocessing

3. Intent and Entity Detection

4. Contextual Analysis

5. Response Generation

6. Action Execution

Firstly user inputs a query via voice or text then the text is divided into tokens and prepared for analysis after that NLU model identifies the intent and extracts related entities and then the system maintains the context by considering previous interactions. The system will generate the response based on intent, entities, and context if the intent of the person is action then the system triggers the important backend processes. There are many advantages of NLu in customer-improved accuracy, personalization, Efficiency, and handling complex queries.

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