Natural language processing is an area of artificial intelligence that focuses on training computers to understand and respond to human language in a way that’s valuable. You might be familiar with how they are being utilized by search engines such as Google’s Gemini or Microsoft’s Copilot. In our guide, we’ll explore different Natural Language Processing techniques and Natural Language Processing jobs.
How Does Natural Language Processing Work?
Natural Language Processing uses a variety of processes such as machine learning to understand and respond to a human input. It combines linguistics with artificial intelligence.
Tokenization in Natural Language Processing
Tokenization is the process of taking a sequence of text and breaking it down into smaller parts, so that it can be understood. Each piece of text that is broken down is known as a token and can be anything from a single letter to a whole word.
Tokenization is a process that’s similar to teaching a child to read by breaking text down into digestible parts.
Example: “The Quick Brown Fox Jumps Over the Lazy Dog” = “T” ”h” ”e” “Q” “u” “I” “c” “k” “B” “r” “o” “w” “n” “F” “o” “x” “J” “u” “m” “p” “s” “O” “v” “e” “r” “t” “h” “e” “L” “a” “z” “y” “D” “o” “g”
Lemmatization in Natural Language Processing
Lemmatization is a type of linguistic processing that defines each word by its core meaning. The root meaning of each word is referred to as its Lemma.
Example: The word “terrible” would have a lemma of “bad”
Stemming in Natural Language Processing
Stemming is the process of shortening a word to a simpler form by removing common suffixes such as ‘ING’ and ‘ED’. This can sometimes result in the stemmed version of the word not being an actual word but a shortened version of the word.
Example: “Speeding” or “Speeded” would become “Speed”
Part of Speech Tagging
Part of speech tagging in natural language modelling is when the sentence structure is analyzed grammatically, and elements of the sentence are broken down into nouns, verbs, adjectives, pronouns and tense.
Example: Jimmy eats cake. Jimmy is a Proper Noun (NNP). Eats is a verb in the present tense (VBZ). Cake is a common noun (NN).
Named Entity Recognition
Named Entity Recognition is a process of assigning identities to specific words or topics, such as organizations, people and places. It detects and identifies real-life examples to help predict trends or analysis.
Example: Marilyn Monroe is a named person, Google is a named organization, USA is a named location, and 1st January 2000 is a named date.
Parsing in Natural Language Processing
Parsing is the analysis of the relationship between words in a sentence or extract of text. Parsing helps Natural Language Processing models to read a prompt and interpret it in a way that can deliver the correct answer.
Example: A prompt to Google Gemini might ask, “How many times did David Beckham play for Manchester United?”. Parsing allows Google to understand the prompt by rephrasing it to a command, such as “What is the total number of appearances David Beckham made for Manchester United?”
Sentiment Analysis
Sentiment Analysis interprets the tone and emotion relating to the input of text. This can be especially useful for chatbots to gain a deeper understanding of customer interactions and whether they are positive, negative or neutral.
Example: Positive Sentiment “I’m pleased my delivery arrived today”, Negative Sentiment “I’m disappointed my delivery only arrived today”, Neutral sentiment “My delivery arrived today”
Topic Modelling
Topic modelling teaches natural language processing models additional context clues relating to different topics. It identifies patterns to understand underlying themes within a piece of text.
Example: Companies like Amazon analyze large amounts of reviews to identify reoccurring themes. A review of a new laptop might have recurring topics such as battery life, screen brightness and processing time.
What is Natural Language Processing Used For?
Natural Language Processing is currently utilized across a number of different industries with the aim of streamlining processes, identifying trends and improving customer relations. The main industries that Natural Language Processing is used for include:
- Healthcare: Extracting information from patient records to help inform treatment.
- Finance and Banking: It can be used for fraud detection and financial analysis.
- Retail and E-commerce: It can enhance personalization with product recommendations and analyzing feedback on products.
- Legal and Compliance: Assists with the analysis of contracts, identifying risks, summarising legal research and monitoring compliance.
- Customer service: Natural Language Processing can benefit any customer service function with chatbots, automated responses and sentiment analysis.
- Manufacturing and supply chain: Natural language processing can enhance trend forecasting and quality control.
Natural Language Processing techniques can process large quantities of data to improve day-to-day operations and save overall costs.
Natural Language Processing Job Roles
The value of Natural Language Processing is being realized by businesses across the globe with the rise of open AI and other Natural Processing models.
Natural Language Processing and Machine Learning job roles can include;
- Data Scientists
- Chatbot developer
- Natural language processing engineer
- Machine learning engineer
- AI engineer
How Can Alexander Daniels Global Help?
Here at Alexander Daniels Global, we’re expert recruiters for industry 4.0, recruiting from entry-level roles to C-suite. If you’re looking for your next role in Natural Language Processing, we’re here to help, see our latest job roles or contact us for more information.
If you’re looking to hire a Natural Language Processing expert in your business, contact our recruitment team today.
