Recently a group of scientists at Seoul University built an NLP (Natural Language Processing) algorithm to translate unstructured EEG reports data into structured tabular data with a 98.5% accuracy in determining the type of seizures (focal or generalized). NLP is a pivotal technology in widening the AI application horizon from chatbots responding to natural human language to applications that can process news articles, corporate reports, and other documents to offer valuable trading related insights. According to Fortune Business Insights, the global NLP market is projected to reach USD 158.04 billion by 2032 at a CAGR of 23.2%. Before we examine the impact of NLP in AI progress let’s understand what NLP is.
What is NLP?
NLP is evolving AI by giving computers the power to truly understand and respond to human language. This capability, in turn, fuels cognitive computing, enabling computers to collect, analyze, and, most importantly, understand data with human-like intuition. NLP achieves this remarkable feat by uniting computational linguistics, machine learning, and deep learning, allowing it to process the subtle nuances of human communication – intent and emotion. We see the impact of NLP all around us, from Google Translate instantly translating languages to voice-controlled assistants like Alexa and Siri responding to our every command, and GPS systems guiding us on our journeys with voice instructions.
Why is Natural Language Processing Important?
Why does Natural Language Processing (NLP) matter? Primarily, because NLP empowers businesses to manage and analyze the vast deluge of text-heavy data that defines today’s information landscape. Previously, the sheer volume of human language online and in databases presented a formidable challenge to computers. However, NLP changes that dynamic by enabling machines to truly understand and work with this previously inaccessible data.
Beyond data analysis, NLP also streamlines critical business operations and boosts employee productivity. For instance, consider the statements “Cloud computing insurance should be part of every service agreement” and “A good SLA ensures an easier night’s sleep — even in the cloud.” NLP now effortlessly identifies that “cloud computing” and “cloud” both refer to the same concept, and understands that “SLA” is the abbreviation for “service-level agreement.” Indeed, this is an ability older machine learning algorithms struggled to achieve. But today’s deep learning and machine learning advances enable computers to interpret these linguistic nuances with far greater accuracy, unlocking insights from ever-larger datasets.
Finally, let’s consider the impact on our daily lives. NLP makes technology more accessible and user-friendly in everyday interactions, such as powering voice assistants like Siri. Now, users can speak naturally rather than relying on rigid commands, confident that GenAI will understand their intent. Let us get a broader understanding of the tasks NLP can perform in the next section.
Tasks That NLP Can Perform
Speech recognition
Speech recognition reliably converts spoken data into a system understandable format. This function is essential for any application that responds to voice commands or answers spoken queries. Speech recognition systems address challenges stemming from variations in human speech patterns, including rapid speech, word merging, diverse accents, and occasional grammatical errors.
Part-of-speech tagging
Part-of-speech tagging, or grammatical tagging, identifies the role of each word within a sentence based on its usage and context. For example, this process categorizes “make” as a verb in the sentence “I can make a dinner” and as a noun in “What make of earrings do you have?”
Word-sense disabmiguation
Word-sense disambiguation selects the appropriate meaning of a word with multiple potential interpretations. It employs semantic analysis to determine the most contextually accurate meaning. For instance, it clarifies whether “make” signifies “meet” in “make the deadline” or “compose” in “make a song.”
Named entity recognition (NER)
Named entity recognition (NER) tags words or phrases as relevant entities. For example, NER systems identify “California” as a location and “Tom” as a person’s name.
Co-reference resolution
Co-reference resolution determines when two words refer to the same entity. A common application involves identifying the person or object to which a pronoun refers (e.g., “she” = “Madonna”). However, it also detects instances where a word such as “mouse” refers to something other than the animal.
Sentiment analysis
Sentiment analysis extracts subjective elements, such as attitudes, emotions, and tones, from text. AI solutions in the insurance sector, for example, utilize this technique to detect fraudulent claims and analyze textual data for inconsistencies, patterns, and anomalies.
Natural language generation
Natural Language Generation (NLG) is a process of producing natural written or spoken language from some underlying non-linguistic representation of information (structured and unstructured data). NLG is used to generate reports, produce BI analysis summary as in Lumenn AI, or reply to user queries in an interactive chatbot like chatGPT.
Industries Using NLP Development Services
Businesses are increasingly turning to NLP to simplify and automate text-heavy processes across a range of functions. Here are several examples of industries that are leveraging NLP development services to gain a competitive advantage:
Healthcare
Faced with an explosion of unstructured data stemming from the shift to electronic health records, hospitals and healthcare systems are leveraging NLP to analyze doctor’s notes, patient histories, and other textual data. This enables researchers to identify critical health trends and empowers clinicians to improve patient care and treatment outcomes.
Legal
Manual document review is time-consuming and error-prone. Law firms are implementing NLP solutions to analyze vast quantities of legal documents and identify key details more efficiently.
Finance
Seeking to stay ahead of the competition, traders and financial analysts are using NLP to automatically extract valuable insights from news articles, corporate reports, and other financial documents. This empowers them to make more informed investment decisions and optimize their portfolios.
Customer service
Striving to improve customer satisfaction and reduce the workload of their customer service teams, large companies are deploying virtual assistants and chatbots powered by NLP to handle common customer inquiries. Sentiment analysis empowers these chatbots to respond in a more humanlike fashion elevating the customer satisfaction.
Insurance
Burdened by extensive paperwork associated with insurance claims, insurance companies are using NLP to automatically sort through reports, forms, and other documents and identify key details. This streamlines the claims process, reduces manual effort, and improves operational efficiency.
How NLP Works
In NLP, the initial step involves segmenting human language into discrete units, processing each segment as an independent thought or concept. Subsequently, the system establishes connections between these units, examining contextual relationships to ascertain both the intent and sentiment conveyed by the input.
Generally, natural language processing proceeds through two distinct phases:
Data pre-processing and algorithm development.
Data pre-processing
Initially, data pre-processing serves to refine the text, eliminating redundant elements and transforming it into a machine-readable format. Specifically, this phase encompasses the following operations:
Tokenization: The decomposition of the text into individual lexical units.
Stop Word Removal: The elimination of superfluous terms that contribute negligibly to the overall meaning, thereby reducing linguistic noise and retaining only terms pertinent to the data.
Lemmatization/Stemming: The reduction of words to their base or root form to facilitate a more comprehensive semantic understanding.
and Part-of-Speech Tagging.
Following the completion of data preparation, dedicated NLP algorithms are employed to process the refined data.
Principal categories of NLP algorithms
A range of natural language processing algorithms finds applications in the field. Among the more prevalent ones, the following are noteworthy:
Rules-Based NLP
As one of the earliest methodologies, rule-based NLP systems operate based on predefined linguistic rules. Typically, these rules are formulated by linguistic experts or engineers. NLP then applies these rules to categorize the language under analysis.
However, in instances where a governing rule is absent, the system is unable to interpret or classify the human language.
Despite this limitation, this algorithm augments its capabilities beyond mere keyword searches by incorporating extensive rule libraries that govern human language, thereby enhancing the accuracy of the results.
Machine Learning Algorithms
In contrast to rule-based approaches, machine learning models learn to perform specific tasks, such as categorizing and analyzing data from previous data and making predictions on their own, eliminating the mandate for explicitly defined rules.
Instead, by presenting training data, machine learning algorithms establish correlations between inputs and their corresponding outputs.
The Advantages of NLP in AI
NLP transforms the way humans interact with computer systems. Traditionally, users were required to utilize specialized computer languages. NLP now allows direct communication with computers through natural human language.
Notable applications of NLP include:
- Automated summarization of large document sets.
- Enhanced data quality through automated error detection and correction.
- Support for voice-activated interfaces and commands.
- Automated sentiment analysis to gauge customer attitudes.
- Automation of routine tasks, such as customer support interactions.
- Analysis of large datasets to reveal actionable insights.
Challenges of Developing NLP
Developing NLP presents several significant challenges due to the evolving and ambiguous nature of human language. Specifically:
Imprecision
Computers require exact commands, unlike human language, which often lacks clarity and is influenced by slang, dialects, and social context. This imprecision hinders computers’ ability to accurately interpret meaning.
Variable tone and inflection
NLP systems struggle to accurately discern tone and inflection. Sarcasm, abstract language, and subtle changes in emphasis can alter meaning, and NLP may fail to recognize these nuances.
Language evolution
The constant evolution of language necessitates ongoing updates to NLP systems. Existing rules become obsolete as language changes, requiring continuous adaptation.
Bias in training data
NLP systems can inherit biases from the data used for training. These biases risk discrimination in sensitive applications, such as healthcare and hiring.
Conclusion
Advancements in Natural Language Processing (NLP) have fundamentally transformed our interactions with machines, creating new opportunities across multiple industries. Transformer-based models like BERT and GPT-3 have demonstrated remarkable capabilities in understanding and generating human language. Moreover, the application of NLP in sectors such as healthcare, education, and customer service illustrate its potential as a transformative force.
As the field of NLP continues to evolve, we can look forward to even more groundbreaking innovations. These developments will bring us closer to achieving seamless human-computer communication and intelligent AI systems that truly comprehend and adapt to human language. In summary, the future of NLP promises to enhance our interactions with technology, making them more intuitive and effective.
