Natural Language Processing in AI Agents: A Comprehensive Guide

What is NLP?

Natural Language Processing (NLP) is a computer science and AI subcategory that uses machine learning (ML) to understand, interpret, and generate text in human language. It combines computational linguistics, human language rule-based modeling, statistical modeling, machine learning, and deep learning.

NLP has revolutionized the generative AI landscape with its communication skills, ability to generate images, and understanding of requests. It is already an everyday part of many; think of Apple’s Siri, Amazon Alexa, or Google’s Google Assistant. They can understand voice commands, voice-operated GPS systems, and question-answering digital assistants.

Why is NLP important?

NLP, or natural language processing, has become an integral part of day-to-day life. It is used in various fields, such as retail (in customer service chatbots) or healthcare (generating a health record summary). Virtual assistants like Siri or Alexa use NLP to listen to instructions, interpret them, find answers, and provide relevant results. Agents like GPT-3 are powerful software that can generate sophisticated content on various topics and empower chatbots to handle consistent conversations.

For instance, Google uses Natural Language Processing to enhance its search engine results, and Facebook and Instagram use it to detect and filter hate speech. Much has already been accomplished in NLP; however, there is still work to be done. Existing systems are prone to biased, inconsistent, and occasionally random behaviour.

Key components of NLP

The key components of NLP include Natural Language Understanding (NLU) and Natural Language Generation (NLG). Natural language understanding focuses on understanding and interpreting human language, and Natural language generation focuses on creating human-like text.

Natural Language Understanding (NLU)

• Lexical analysis – Analyzes every word and its meaning.
• Syntactic analysis – Analyzes the structure of the sentence and the grammar.
• Semantic analysis – Analyzes the meaning of words and phrases in the context.
• Discourse integration – Understands how two sentences are related in a larger text.
• Pragmatic analysis – Consider the social and communicative context of language.

Natural Language Generation

• Text planning – Decides what information to cover in the generated text.
• Sentence planning – Structures the sentence grammatically.
• Text realization – Generates the final content.

Other components

• Tokenization – The NLP breaks the raw text into units known as tokens, such as words, punctuations, and symbols, making the process more digestible.
• Part-of-speech (POS) tagging – Assigns a grammatical category to each token in the text. These grammatical categories include noun, verb, adjective, adverb, etc. The tokens can be words, punctuation marks, or even subwords.
• Named entity recognition (NER) – This task identifies and categorizes named entities in text, including people, organizations, locations, time, quantity, and monetary values.
• Sentiment analysis – It can determine the tone or attitude expressed in a piece of text. It can categorize sentiments as positive, negative, or neutral. It can even delve into more granular emotions like anger, joy, or sadness.
• Machine Learning/Deep Learning – NLP depends on machine learning and deep learning algorithms to analyze data and identify patterns for making predictions.
• Machine translation – Translating text from one language to another.

Types of NLP techniques

Natural language processing involves various techniques to understand and process human language. These techniques are:

Rule-based approaches

They rely on linguistic rules defined by humans to analyze and manipulate text. These rules are determined based on grammar, syntax, and semantics. AI experts manually set these rules or patterns to identify linguistic features or perform certain tasks. Rule-based approaches can be effective for specific, well-defined tasks and provide transparent and interpretable results. They can perform well with limited data.

Statistical methods

Large text collections are available, making statistical approaches gain dominance. These methods use mathematical models and statistical inferences to learn patterns and relationships in language data. Statistical methods include analyzing large datasets to calculate probability and linguistic unit frequencies and their co-occurrence. These statistics are used to make future predictions and text decisions. Unlike a rule-based approach, this method can handle the variability and ambiguity of natural language. They can learn from the data and improve their performance as data becomes available.

Machine learning

This is one of the major aspects in natural language processing. Machine learning techniques, such as deep learning, enable computers to learn from data without human interference for coding. Machine learning algorithms are trained using large datasets, allowing them to learn from complex patterns and language representations. The techniques include supervised learning, unsupervised learning, and deep learning. Machine learning helps achieve enhanced performance, automatically learn complex features from data, and generalizes well to new data when trained properly.

Where is natural language processing used?

NLP is used in various tasks, including processing human text and voice data, which helps computers make sense. Some of these tasks include:

Everyday applications

• Chatbots – They can understand customer queries and provide automated service.
• Voice assistants – Technologies like Siri, Alexa, and Google Assistant rely on NLP to understand and respond to voice commands.
• Text translation – Applications like Google Translate can translate text between different languages.
• Spam filters – Analysing language patterns with NLP to identify and filter spam emails.
• Text summarization – Condensing large amounts of text into concise summaries.
• Grammar checker – Identify and correct errors in written text, improving content clarity and engagement.
• Sentence autocomplete – Automatically complete sentences, leveraging the suggestions based on the input.

Business applications

• Sentiment analysis – Analyze customer feedback from various sources, including emails, social media, or reviews, to understand their sentiments about a product or service.
• Text extraction – Extract key information from an unstructured document, like emails or reports.
• Data analysis – Analyze large datasets to identify trends, patterns, and insights.
• Customer support automation – NLP-powered chatbots can handle common customer inquiries, freeing up human agents for more complex issues.
• Document processing – Automate large volumes of document processing.
• Social media monitoring – Monitor conversations to track brand sentiment and identify emerging trends.
• Supply chain management – Using NLP for demand forecasting, inventory management, and supplier selection.
• Fraud detection – Analyze text from financial documents to detect fraudulent transactions.

How does NLP work?

Natural Language Processing (NLP) uses a combination of computational linguistics, machine learning, and deep learning to process human language (text or speech).

Computational linguistics

A science that includes understanding and building human language models on software and computers. Computational linguistics techniques, including syntactic and semantic analysis, can help you create frameworks to understand conversational human language. The tools based on computational linguistics include language translation, text-to-speech synthesizer, and speech recognition.

Machine learning

Machine learning is a technique that trains computers with sample datasets to improve their efficiency. The programmers use machine learning to inculcate several human language features, including sarcasm, metaphors, sentence variations, grammar, and usage exceptions. This allows NLP applications to recognize and understand these features.

Deep learning

Deep learning is a specific field of machine learning that involves neural networks. These networks have processing nodes similar to neurons in the human brain. Deep learning can help computers recognize, classify, and correlate complex patterns in the input.

Natural Language Processing Steps

NLP combines computational techniques to analyze, understand, and generate human language for machine processing. It starts with collecting and preparing unstructured text and speech data from multiple sources, including cloud data, warehouses, surveys, emails, or internal business processes apps.

Pre-processing

The text is processed for a specific task to improve model performance and translate human text into a language that machines understand. The data is processed using various techniques, including:

• Stemming and lemmatization – Deduce words to their root form, e.g., running becomes run. It analyzes a word’s morphology using vocabulary from a dictionary. Libraries like spaCy and NLTK provide stemming and lemmatization.
• Sentence segmentation – Fragments a large text into linguistically meaningful sentence units. The end of a sentence is marked by a period, which can’t be ignored. A period can mark an abbreviation and the termination of the sentence. In this case, the period should be part of the token.
• Stop word removal – Removes commonly occurring words that do not provide much information.
• Tokenization – Split text into individual words and fragments, consisting of a word index and the tokenized text. They are presented as numerical tokens used for deep learning methods.
• Feature extraction – Converting raw text into numerical representations that machines can interpret.

Training

AI engineers use pre-processed data and machine learning to train large language models to perform tasks based on contextual information. Training includes feeding large data samples to improve the model’s accuracy. The effectiveness of the NLP model is continuously refined through evaluation, validation, and fine-tuning to enhance its relevance in real-world applications.

Deployment and inference

Experts in AI and NLP implement or incorporate the model into an existing production environment. The model receives input and predicts output based on the use case. The NLP application is run on live data to obtain the required output.

How does Master Software Solutions help your NLP tasks?

Master Software Solutions provides complete AI agent development services using artificial intelligence and machine learning. Our services include consulting, custom AI agent development, integration, training and optimization, behavioral modelling, and deployment and scalability. We have built 100+ AI solutions for a broader industrial scope that aims to automate and streamline your business.

Contact us to discuss AI agent needs for your business and see how we can help you.