Every minute of every day people make 5.9 million Google searches, post 66,000 photos to Instagram, upload 500 hours of video to YouTube, and send 231.4 million emails — and this is just a fraction of the new data being created. By 2025, global data creation is projected to grow to more than 180 zettabytes — or 180 trillion gigabytes — annually.
On one hand, this means that, with every passing minute, there is more data available to us than ever before. But for businesses and organizations eager to derive meaning and insights from this data, it can present an overwhelming challenge.
Consider the fact that 80 to 90% of this data is unstructured, meaning it doesn’t fit into tidy rows and columns on a spreadsheet and can’t be stored in a traditional relational database. Unstructured data is amorphous, often language- and text-heavy, and much more difficult for computers to process.
But as artificial intelligence technology has continued to advance in recent years, so too has our ability to make sense of all this unstructured data.
That’s where natural language processing comes in.
Natural language processing, or NLP, enables computers to interpret, understand, and generate human language. With NLP, computers can analyze vast amounts of unstructured data — like emails, chat logs, social media posts, and medical records — and extract meaningful insights and patterns. It also enables generative AI tools like ChatGPT and DALL-E to create new data in the form of novel text, code, and images.
NLP is a powerful tool that has already transformed industries like finance, healthcare, and marketing, and its potential applications are virtually limitless. But what exactly is natural language processing? How does it work, and what are the challenges facing the field? More importantly, what are the skills needed to build a career and thrive in NLP? In this blog post, we’ll answer these questions and more.
What is Natural Language Processing?
Natural language processing is a branch of artificial intelligence that focuses on the interaction between computers and human language. NLP enables machines to understand, interpret, and even generate natural language, just as humans do. At its core, NLP involves breaking down human language into its constituent parts, such as words, phrases, and sentences, and using algorithms to analyze these parts and extract meaning from them.
One of the key challenges in NLP is that human language is incredibly complex and can vary widely depending on the context and culture. For example, the same word can have different meanings depending on the context in which it is used, and the same sentence can be interpreted in different ways depending on the person reading it.
To overcome these challenges, NLP relies on a variety of techniques, including machine learning, deep learning, and natural language understanding. Machine learning involves training algorithms on large datasets of annotated text data to recognize patterns and learn to make predictions about new text data. Deep learning involves building neural networks that can learn to process and analyze natural language data in a way that mimics the human brain. Natural language understanding involves designing algorithms that can recognize the underlying meaning and intent behind natural language text.
How is Natural Language Processing Used?
Natural Language Processing has many applications across various industries, from business to healthcare and science, and even in social media and customer service. In this section, we’ll explore some of the most common applications of NLP.
NLP Applications in Business
NLP is widely used in business to analyze and interpret customer feedback, sentiment, and behavior. Companies can use NLP algorithms to analyze customer reviews, social media posts, and other unstructured data to gain insights into customer preferences and opinions, which can be used to develop new products and features.
NLP Applications in Healthcare and Science
In healthcare and life sciences, NLP is used to analyze and interpret medical data, including clinical notes, electronic health records, and research articles. NLP algorithms can help healthcare professionals identify patterns and trends in patient data, develop personalized treatment plans, and even predict disease outcomes.
NLP in Social Media and Customer Service
NLP is increasingly being used in social media and customer service to analyze and respond to customer feedback and complaints. Companies can use NLP algorithms to monitor social media conversations and identify opportunities to improve customer service. NLP can also be used to automate customer service tasks such as chatbots — like ChatGPT — and virtual assistants, saving companies time and resources.
Common Techniques Used in Natural Language Processing
NLP involves a wide range of techniques and technologies that are used to process and analyze natural language data. Here are some of the most common techniques used in NLP.
Tokenization: This involves breaking up a piece of text into individual words, phrases, or symbols, known as tokens. Tokenization is the first step in many NLP tasks and is used to create a structured representation of the text.
Part-of-speech (POS) tagging: This is the process of assigning a grammatical label to each word in a piece of text, such as noun, verb, adjective, or adverb. POS tagging is used in many NLP tasks, such as named entity recognition and sentiment analysis.
Named entity recognition (NER): This technique involves identifying and categorizing named entities in a piece of text, such as people, organizations, and locations. NER is used in many applications, such as information extraction and question answering.
Sentiment analysis: This technique assists with identifying the sentiment expressed in a piece of text, such as positive, negative, or neutral. Sentiment analysis is used in applications such as social media monitoring and customer feedback analysis.
Machine translation: This involves automatically translating text from one language to another. Machine translation enables activities like language learning and cross-cultural communication.
Topic modeling: This is the process of analyzing a set of data or documents — such as legal contracts or health records — to identify common themes and group them into similar clusters. Topic modeling is used in many applications, such as content analysis and information retrieval.
Text classification: This technique assigns a predefined label to a piece of text, such as spam or not spam, or news or opinion. Text classification is used in many applications, such as email filtering and news categorization.
These are just a few examples of the many technologies used in NLP. Each technology has its own strengths and limitations and is best suited for specific tasks and applications. By combining these technologies, NLP practitioners can develop powerful tools for analyzing and processing natural language data.
Challenges in Natural Language Processing
While NLP has made significant progress over the years, it still faces many challenges and limitations. One of the biggest challenges is the ambiguity and complexity of human language. Human language is full of nuances, idioms, and cultural references that can be difficult for machines to understand.
Another challenge is the lack of labeled data. Labeled data refers to data that has been assigned one or more labels that identify certain properties or characteristics, like whether a photo contains a plane or a bicycle, or which words were spoken in an audio recording. NLP algorithms rely on large amounts of labeled data to learn patterns and make predictions. However, in many cases, labeled data is not available or is difficult to obtain.
Furthermore, NLP faces the challenge of generalization. NLP models trained on a specific task or domain may not perform well on other tasks or domains. This is because language use and context can vary significantly between different domains and tasks.
For example, let’s say you have an NLP model that has been trained to recognize sentiment in product reviews. The model has been trained on a large dataset of reviews for smartphones. When you test the model on new data, it performs well and accurately predicts the sentiment of reviews for smartphones.
However, when you try to use the same model to predict sentiment in reviews for laptops, the model doesn’t perform as well. This is because the language used in reviews for laptops may be different from the language used in reviews for smartphones. The model may not be able to generalize well to a new domain because it has only been trained on a specific type of data.
Finally, privacy concerns are also a challenge in NLP. NLP algorithms often require access to personal data such as social media posts, emails, and chat logs. This raises important questions about privacy and data protection.
To address these challenges, researchers and practitioners in the field are working on developing more sophisticated NLP algorithms that can better understand the nuances of human language, as well as new methods for collecting and labeling data. They are also exploring ways to improve generalization by developing models that can adapt to different domains and tasks. This involves developing models that can learn from fewer labeled examples, as well as techniques for transfer learning, where knowledge learned from one task can be applied to another task. Finally, they are working to address privacy concerns by developing methods for anonymizing and protecting personal data.
Career Opportunities in Natural Language Processing
Natural language processing is a rapidly growing discipline with a wide range of career opportunities. Here are some examples of career paths you can take in NLP:
- NLP Engineer: An NLP engineer designs and develops NLP models and algorithms. They are responsible for creating and testing NLP systems to solve real-world problems.
- Computational Linguist: A computational linguist works on developing NLP models that can accurately understand and generate human language. They analyze linguistic data and work on developing tools for natural language understanding and generation.
- Data Scientist: A data scientist with NLP expertise works on analyzing and extracting insights from large datasets of natural language data. They use machine learning algorithms and NLP techniques to gain insights from unstructured data.
- Speech Recognition Engineer: A speech recognition engineer works on developing systems that can accurately transcribe speech into text. They use NLP techniques to analyze speech and convert it into structured data.
Skills Required for NLP Jobs
To succeed in a career in NLP, you will need a combination of technical and soft skills. Here are some of the most important skills required for NLP jobs:
- Programming: NLP requires expertise in programming languages such as Python, Java, and C++. Familiarity with machine learning libraries like TensorFlow, PyTorch, and scikit-learn is also essential.
- Linguistics: A solid understanding of linguistics and language theory is essential for developing NLP models. This includes knowledge of syntax, semantics, and pragmatics.
- Mathematics and Statistics: NLP involves the use of mathematical models and statistical analysis to process natural language data. Knowledge of linear algebra, calculus, probability theory, and statistics is essential.
- Communication: NLP professionals must be able to communicate effectively with both technical and non-technical stakeholders. This includes presenting findings and recommendations to management and collaborating with other departments.
Natural language processing is a rapidly growing field that has tremendous potential for organizations and businesses in a wide range of industries. As natural language processing continues to advance, we expect to see even more innovative applications and use cases emerge.
To learn more about artificial intelligence and the many types of professionals involved in this field, check out HackerRank’s role directory and explore our library of up-to-date resources.