Supervised vs Unsupervised Learning Methods in NLP

According to the glossary of AI terms, NLP also known as Natural Language Processing is extensively used to address a variety of human language challenges for those systems primarily related to Syntax Analysis (arrangement of words in a sentence such that they make grammatical sense) like Lemmatization, Word Segmentation, Part-of-Speech (PoS) Tagging, etc., and Semantic Analysis (understand the meaning and interpretation of words and how sentences are structured) like Named-entity-Recognition (NER), Word-Sense Disambiguation, Natural Language Generation (NLG), and more. AI Chatbots and AI Virtual Assistants use either one or a balanced combination of the two families of NLP Learning.

Therefore, when inquiring whether is NLP utilizing unsupervised or supervised learning techniques? The concise answer is that NLP employs both Supervised Learning and Unsupervised Learning. In this article, we delve into the reasons behind the use of each approach and the scenarios in which they are most effectively applied in NLP.

AI Virtual Assistants with Supervised Learning are trained using data that is well-labeled (or tagged). During training, those systems learn the best mapping function between known data input and the expected known output. Supervised NLP models then use the best approximating mapping learned during training to analyze unforeseen input data (never seen before) to accurately predict the corresponding output.

Usually, Supervised Learning models require extensive and iterative optimization cycles to adjust the input-output mapping until they converge to an expected and well-accepted level of performance. This type of learning keeps the word “supervised” because its way of learning from training data mimics the same process of a teacher supervising the end-to-end learning process. Supervised Learning models are typically capable of achieving excellent levels of performance but only when enough labeled data is available.

Furthermore, the building, scaling, deploying, and maintaining of accurate supervised learning models takes time and technical expertise from a team of highly skilled data scientists. For example, a typical task delivered by a supervised learning model for AI chatbot / Virtual Assistants is the classification via a variety of different algorithms like Support Vector Machine, Random Forest, Classification Trees, etc. of an input user utterance into a known class of user intents.

The precision achieved by those techniques is remarkable though the shortfall is limited coverage of intent classes to only those for which labeled data is available for training.

To overcome the limitations of Supervised Learning, academia, and industry started pivoting towards the more advanced (but more computationally complex) Unsupervised Learning which promises effective learning using unlabeled data (no labeled data is required for training) and no human supervision (no data scientist or high-technical expertise is required). This is an important advantage compared to Supervised Learning, as unlabeled text in digital form is in abundance, but labeled datasets are usually expensive to construct or acquire, especially for common NLP tasks like PoS tagging or Syntactic Parsing.

Unsupervised Learning models are equipped with all the needed intelligence and automation to work on their own and automatically discover information, structure, and patterns from the data itself. This allows for the Unsupervised NLP to shine.

The most popular applications of Unsupervised Learning in Enterprise AI chatbots, AI Virtual Assistants, and Enterprise AI Copilots are clustering (like K-mean, Mean-Shift, Density-based, Spectral clustering, etc.) and association rules methods. Clustering is typically used to automatically group semantically similar user utterances to accelerate the derivation and verification of an underneath common user intent (notice derivation of a new class, not classification into an existing class).

Unsupervised Learning is also used for association rules mining which aims at discovering relationships between features directly from data. This technique is typically used to automatically extract existing dependencies between named entities from input user utterances, dependencies of intents across a set of user utterances part of the same user/system session, or dependencies of questions and answers from conversational logs capturing the interactions between users and live agents during the problem troubleshooting process.

Even though the benefits and level of automation brought by Unsupervised Learning are large and technically very intriguing, Unsupervised Learning, in general, is less accurate and trustworthy compared to Supervised Learning. Indeed, the most advanced AI Assist technologies in the market strive to achieve the right level of balance between the two technologies, which when exploited correctly can deliver the accuracy and precision of Supervised Learning (tasks for which labeled data is available) coupled with the self-automation of unsupervised learning (tasks for which no labeled data is available).

Aisera offers the most feature-comprehensive and technology-advanced AI Virtual Assistant solution for self-service automation in the market perfectly blending together Supervised Learning and Unsupervised Learning, Natural Language Understanding (NLU),  AI Virtual Assistant technology, Conversational AI (cognitive search) and Conversational Automation into one SaaS cloud offer for IT Service Desk and AI Customer Services. Aisera’s proprietary unsupervised NLP/NLU technology, User Behavioral Intelligence, and Sentiment Analytics are protected by several patent-pending applications.

The distinction between supervised and unsupervised learning in NLP is not just academic but fundamentally impacts the development and effectiveness of AI-driven platforms like AiseraGPT and AI copilots. These technologies, by leveraging both learning methods, offer a robust framework that balances precision with discovery, enabling them to not only understand and respond to user inputs accurately but also to adapt and uncover new insights without direct human oversight.

As the field advances, the integration of both supervised and unsupervised learning approaches continues to shape the frontier of conversational AI, making technologies like Aisera’s AI copilot indispensable tools for AI-enhanced employee experience and customer experience along with operational efficiency. You can experience a tailored AI demo for your enterprise today!