Natural Language Processing
What is Natural Language Processing?
Natural Language Processing is a branch of artificial intelligence (AI) that enables computer systems to understand, interpret, control, and comprehend human language. It uses computational linguistics in combination with machine learning and deep learning models to process human language. The human language is processed in text or voice data to understand the intent/sentiment of the users.
Computer programs facilitate the translation of text from one language to another, answer commands, and summarize large data volumes in real time. For example, enterprises use text and voice formats to communicate via emails, social media, audio/video, etc.
Importance of NLP
- Process, evaluate, and archive information
- Deploy chatbots for customer service
- Classification and extraction of text
Natural language processing leverages the following techniques to analyze human languages.
- Sentiment analysis: Analyses text to recognize sentiments and understand customer opinion.
- Summarization: Condenses large pieces of text by preserving the key points and information.
- Keyword extraction: Identifies and extracts the essential keywords for SEO, data visualization, and analytics.
- Tokenization: Divides words and characters into smaller units called tokens for preprocessing, segmentation, and language processing.
How does natural language processing work?
Natural language processing (NLP) uses computer algorithms to process and analyze human language such that computers can understand and respond by comprehending and creating meaningful texts, messages, and conversations.
Natural language processing algorithms deploy different tools and techniques to comprehend and understand human language. All these techniques extract specific information from the specified text. These techniques include tokenization, part-of-speech tagging, syntactic parsing, named entity recognition, semantic analysis, and language generation.
Tokenization is the initial step in NLP, where the text is divided into individual words or phrases called tokens. By dividing the text into tokens, the algorithms get a basic understanding of the structure and context of the text, making it easier to process and analyze. The word tokens are parted by blank spaces and sentence tokens by stops. High-level tokenization is used for collocations (words that go together), for example, fast food.
Part-of-speech tagging assigns appropriate labels to each token, indicating its grammatical category (e.g., noun, verb, adjective, pronoun, preposition, conjunction). This helps to understand the grammatical structure of a sentence for analysis. Syntactic parsing analyzes the sentence structure to understand the relationship between words and phrases. It identifies the subject, predicate, and object in the sentence, allowing the algorithm to understand the meaning of sentences.
Furthermore, NER (Named Entity Recognition) identifies and categorizes named entities such as names, locations, or organizations. It extracts crucial information from the next and facilitates information extraction and sentiment analysis. For example, in the sentence "George teaches English," a person (George) is related to the subject (English) by the semantic category "teaches."
The semantic analysis goes beyond syntax to understand the meaning of the text and extract relevant information. It analyzes the text and interprets the intended purpose. Lastly, language generation involves creating human-like responses or generating coherent text using the data extracted from previous steps.
What can NLP be used for?
Natural language processing can be used across different industries, such as:
Healthcare: NLP can extract and analyze medical information from clinical notes, research papers, and patient records to enhance document efficiency and accuracy. It analyses the patient's symptoms and medical history to support diagnosis and treatment recommendations. It automates tasks such as medical coding and billing, improves clinical decision-making by analyzing patient symptoms and treatment records, and even assists in extracting valuable insights from large-scale medical data for clinical research.
Finance: NLP plays an instrumental role in analyzing financial news, summarizing analyst reports, extract information from financial documents such as annual reports and regulatory filing. Analyzing text data with suspicious patterns helps prevent security breaches and fraudulent activities.
NLP can also assist risk management by extracting and analyzing information from legal contracts and financial agreements. NLP-powered chatbots help deliver personalized financial advisory services and automate customer support processes in the banking and finance sector.
eCommerce and retail: NLP analyses the customer feedback and reviews to understand the product sentiments and find ways to improve the product/service. Also, you can send personalized product recommendations depending on customer preferences and browsing history. With the help of NLP, businesses can monitor competitor processes and market trends.
Customer service: NLP can improve customer experiences and streamline operations. Chatbots and virtual assistants powered by NLP can understand customer queries, provide relevant responses, and handle basic transactions. NLP can also analyze customer feedback, social media posts, or customer support interactions to gain insights into customer sentiment and refine service quality.
Marketing and advertising: NLP can help automate content creation for marketing campaigns, email marketing, and product descriptions. Also, it helps track the brand presence on social media channels to manage reputation and build a solid customer base.
Education: Educational institutes can use NLP to grade assignments and essays, providing quick feedback to learners. Also, it can help them to understand grammar, pronunciation, and vocabulary.
Human resources: Organizations can use NLP to scan resumes and job applications to choose the ideal candidates by identifying the right skills and qualifications. They can assess the employee feedback surveys to understand employee sentiment and engagement.
Government and public services: NLP analyses government documents and legislative texts to pull information, assess policy impacts, and improve decision-making.
How can we utilize NLP?
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