Giving machines the ability to understand the language that humans use in everyday life is one of the greatest challenges of technology. While reading and understanding a sentence may seem simple for us, for computers this process requires complex algorithms and sophisticated models. This is where Natural Language Understanding (NLU) comes in, creating the technological infrastructure that enables artificial intelligence systems to actually "understand" human language, not just process it.
NLU (Natural Language Understanding) is an artificial intelligence technology that refers to the ability of machines to understand, interpret and respond appropriately to human language. NLU extracts the meaning of text or speech expressed by users in natural language and translates it into forms that computers can process.
NLU falls under the broader concept of Natural Language Processing (NLP). The main difference between NLP and NLU is that NLP focuses on processing and manipulating language, while NLU focuses on extracting meaning and intention from language. While NLP involves text segmentation, tagging, etc., NLU tries to understand the true meaning, intent and context behind a statement.
According to the Artificial Intelligence Index Report published by Stanford University in 2023, NLU systems have approached the human level in some language comprehension tasks. In particular, NLU models specialized in certain areas can reach accuracy rates of over 95%.
Syntactic Analysis
Syntactic analysis examines the structural organization of sentences and determines the relationships between words in a sentence. In this process, the sentence structure is analyzed using the grammar and syntax rules of the language.
Example: When the sentence "Ali bought bread from the market." is syntactically analyzed:
Semantic Analysis
Semantic analysis focuses on analyzing the meaning of words and sentences. At this stage, features such as semantic relations between words, synonyms, antonyms are taken into account.
For example, it is the task of semantic analysis to understand whether the word "bank" refers to a "financial institution" or a "riverbank". Semantic analysis also tries to determine the overall purpose of the sentence.
Contextual Analysis
Contextual analysis evaluates the meaning of expressions in the text according to the context in which they are found. The same expression may have different meanings in different contexts.
For example, the expression "It's too hot!" can be a complaint when talking about air conditioning settings, or a warning about the temperature of food when eating. Contextual analysis aims to make the right interpretation using information such as dialogue history, user profile, time and location.
Sentiment Analysis
Sentiment analysis tries to determine the emotional tone of the text and the author's feelings. By detecting positive, negative or neutral emotional expressions in the text, it provides information about the user's satisfaction, complaints or other emotional states.
According to a study published in the International Journal of Computational Linguistics in 2023, modern NLU systems can achieve 87.6% accuracy in sentiment analysis in Turkish texts.
Machine Learning Based Approaches
Previous versions of NLU systems were based on rule-based systems. Today, however, machine learning and especially deep learning approaches have become dominant. Machine learning offers the possibility to improve linguistic models by learning from large data. Common machine learning algorithms such as Naive Bayes, Support Vector Machines (SVM) and Decision Trees are used for simple NLU tasks.
Deep Learning Models
Deep learning has led to groundbreaking developments in the field of NLU. In particular, architectures such as Recurrent Neural Networks (RNN), Long-Short Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) have significantly improved NLU performance with their ability to learn long-distance dependencies and complex structures in text.
Transformers and BERT
BERT (Bidirectional Encoder Representations from Transformers), introduced by Google in 2018, is a language model that revolutionized the field of NLU. Models such as BERT and GPT (Generative Pre-Trained Transformer) represent the current state-of-the-art in NLU.
According to the 2023 report published by ACL (Association for Computational Linguistics), Transformer-based models perform on average 18% better on NLU tasks than traditional deep learning models.
Recent Technological Developments
In recent years, especially few-shot learning and zero-shot learning techniques have enabled NLU systems to achieve better results with less data. In addition, multilingual models have improved NLU capabilities in various languages by transferring knowledge between different languages.
Use in Finance
In the finance sector, NLU is actively used in customer service automation, investment analysis and risk assessments. Chatbots and virtual assistants allow customers to perform transactions such as account balance inquiries, money transfers and loan applications through natural language.
According to McKinsey's "AI in Banking" (2023) report, NLU applications in the banking sector have saved up to 30% in customer service costs and increased customer satisfaction by 25%.
Use in Retail
In the retail sector, NLU creates value in the areas of analyzing customer feedback, product recommendations and customer service. In-store assistants and virtual assistants on online shopping platforms can understand customers' natural language product searches and recommend the most suitable products.
According to Deloitte's "Retail Trends 2024" report, conversion rates increased by 23% in retail businesses using NLU-powered virtual assistants.
Use in E-commerce
In e-commerce platforms, NLU plays a critical role in product search optimization, customer support and personalized marketing strategies. Thanks to NLU, customers' natural language queries such as "black, tall, stiletto heels" can be understood and the most suitable products can be offered.
According to Forbes' "E-commerce AI Implementation" (2023) research, NLU-powered search functions can increase conversion rates on e-commerce sites by up to 38%. In addition, analyzing customer reviews with NLU provides valuable input to product development processes.
Uses in Manufacturing
In the manufacturing sector, NLU is used to analyze quality control reports, process maintenance instructions and automate supply chain communication. Robots working with voice commands in a factory environment can understand complex instructions thanks to NLU.
According to PwC's "Industry 4.0" (2023) report, NLU-enabled manufacturing processes reduce documentation and reporting time by 40%.
Uses in Telecommunications
Telecommunications companies utilize NLU technologies in areas such as customer support lines, bill inquiries and tariff changes. NLU enables more effective and faster customer service.
According to Ericsson's "Telecom Customer Experience" (2024) report, NLU-based customer service solutions have reduced the resolution time in call centers by 35%. Operators such as Türk Telekom and Turkcell have integrated NLU-powered chatbots into their customer service processes.
Evaluation of NLU Systems
Performance Metrics
The performance of NLU systems is measured by different metrics. These metrics include accuracy, precision, recall, F1 score and BLEU score. In addition, intention recognition accuracy and entity extraction performance are also important evaluation criteria.
Challenges
NLU systems face challenges such as ambiguity, polysemy and idiomatic expressions. In morphologically rich languages such as Turkish, the complex relationships between word roots and affixes create additional challenges.
According to research presented at the Semeval-2023 conference, idiomatic expressions and metaphors can degrade the performance of NLU systems by up to 30%. Therefore, continuous improvements are being made in contextual semantic inference.
Solution Approaches
Various approaches have been developed to overcome the challenges faced in NLU systems:
Context Enrichment: Enriching the context with additional data such as dialogue history, user profile and domain knowledge
Hybrid Models: Combining machine learning approaches with rule-based systems
Continuous Learning: Systems that continuously improve themselves by learning from user interactions
Domain Adaptation: Customization of general language models to specific domains
Requirements and Preparation
In order to successfully integrate the NLU system, clear requirements must first be determined. Questions such as what language or languages will be supported, what intentions and entities need to be recognized, on what platforms the system will run, etc. must be answered.
In the data collection and preparation phase, sufficient quantity and quality of data must be collected to train the NLU model. In particular, data sets containing domain-specific expressions and terms are critical for the system to be successful in the relevant domain.
Integration Process
Incorporation of the NLU system usually involves the following steps:
NLU Platform Selection: Selecting the appropriate platform such as Dialogflow, LUIS, Wit.ai or a custom developed system
Intent and Entity Identification: Identifying the intentions that the system will understand and the entities that it will recognize
Preparing Training Data: Preparing enough example sentences for each intention
Model Training: Training the NLU model with the prepared data
Integration APIs: Configuring APIs that enable the NLU system to communicate with other systems
Testing and Optimization
After the integration of the NLU system, extensive testing processes should be performed. Tests should be conducted with different user scenarios and edge cases.
During the optimization process, the performance of the system should be continuously monitored and improved. Real data collected from users' interactions with the system should be used to improve the model.
Maintenance and Updates
NLU systems must be updated regularly to adapt to changes in language usage, new terms, and changes in user behavior over time. During the maintenance process, the performance of the system should be monitored and retrained as necessary.
As NLU technologies continue to develop rapidly, many exciting developments await us in the future. MIT Technology Review's "AI Language Models: Future Trends" (2024) report highlights the following trends in the field of NLU:
Multimodal Understanding
NLU systems that combine different modalities such as text, audio, image and video are being developed. These systems will also be able to understand non-verbal cues in human communication.
Autonomous Learning
NLU systems that can learn on their own with minimal human intervention will need less labeled data and adapt faster.
Personalized NLU: Personalized NLU systems that can adapt to users' linguistic habits, preferences and communication styles.
Transparent and Explainable NLU: Transparent NLU systems that can explain their decisions and inferences to build confidence, especially in critical areas such as medicine, finance and law.
Natural Language Understanding technologies will revolutionize human-machine interaction by making it more natural and intuitive, from digital assistants to autonomous systems. The ethical and responsible use of these technologies is critical for the sustainability of future applications.
Natural Language Understanding is one of the most important steps towards artificial intelligence systems becoming "thinking machines". This technology transforms machines from being devices that only process commands to assistants that can have meaningful interactions with humans. For businesses and developers, integrating NLU technologies into their products and services will not only provide a competitive advantage but also significantly improve the user experience.
Dark Data is the name given to data that companies collect but is not used, analyzed, or evaluated.
Predictive analysis is the analysis of big data to make predictions and determine the likelihood of future outcomes, trends, or events occurring.
Retail analytics is the analysis of data generated by retail transactions in order to make profitable business decisions.
We work with leading companies in the field of Turkey by developing more than 200 successful projects with more than 120 leading companies in the sector.
Take your place among our successful business partners.
Fill out the form so that our solution consultants can reach you as quickly as possible.
By combining our 30+ years of experience and experience with the centers of excellence in our ecosystem, we prepare your organization to lead the digital transformation!
