8 Steps To Using Both NLP & NLU In Your Chatbot Medium

• susanti
• April 21, 2025June 25, 2025
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NLP vs NLU: Whats The Difference? BMC Software Blogs

A chatbot must be seen within an organization as a Conversational AI interface and the aim is to further the conversation and give the user guidelines to take the conversation forward. GPT-3 converted this quite large paragraph into six key words or themes. You can configure the environment to be conservative and select only keywords from the text.

For example, in NLU, various ML algorithms are used to identify the sentiment, perform Name Entity Recognition (NER), process semantics, etc. NLU algorithms often operate on text that has already been standardized by text pre-processing steps. Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently. Help your business get on the right track to analyze and infuse your data at scale for AI. NLG, on the other hand, is above NLU, which can offer more fluidic, engaging, and exciting responses to users as a normal human would give.

Step 5: Parse Unstructured Data

They don’t just translate but understand the speech/text input, get smarter and sharper with every conversation and pick up on chat history and patterns. With the general advancement of linguistics, chatbots can be deployed to discern not just intents and meanings, but also to better understand sentiments, sarcasm, and even tone of voice. When all these models are processed together and facilitated with data in voice or text form, it generates intelligent results, and the software becomes capable of understanding human language.

If a developer wants to build a simple chatbot that produces a series of programmed responses, they could use NLP along with a few machine learning techniques. However, if a developer wants to build an intelligent contextual assistant capable of having sophisticated natural-sounding conversations with users, they would need NLU. NLU is the component that allows the contextual assistant to understand the intent of each utterance by a user. Without it, the assistant won’t be able to understand what a user means throughout a conversation. And if the assistant doesn’t understand what the user means, it won’t respond appropriately or at all in some cases. IBM Watson NLP Library for Embed, powered by Intel processors and optimized with Intel software tools, uses deep learning techniques to extract meaning and meta data from unstructured data.

NLP vs NLU: What’s The Difference?

To understand the sentence correctly, the word order is important, we cannot only look at the words and their part of speech. In NLP, a named entity is a real-world object, such as people, places, companies, products etc. Recognizing entities in the user’s input helps you to craft more useful, targeted responses. Intents can be seen as verbs (the action a user wants to execute), entities represent nouns (for example; the city, the date, the time, the brand, the product.).

• Now, consider that this task is even more difficult for machines, which cannot understand human language in its natural form.
• Let us know more about them in-depth and learn about each technology and its application in the blog.
• Without it, the assistant won’t be able to understand what a user means throughout a conversation.
• By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly.

Natural language processing works by taking unstructured data and converting it into a structured data format. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling. NLU goes beyond the basic processing of language and is meant to comprehend and extract meaning from text or speech. As a result, NLU  deals with more advanced tasks like semantic analysis, coreference resolution, and intent recognition. Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write.

Ecommerce websites rely heavily on sentiment analysis of the reviews and feedback from the users—was a review positive, negative, or neutral? Here, they need to know what was said and they also need to understand what was meant. NLP focuses on processing the text in a literal sense, like what was said. Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant. From the computer’s point of view, any natural language is a free form text.

NLP, NLU, and NLG all come under the field of AI and are used for developing various AI applications. Let us know more about them in-depth and learn about each technology and its application in the blog. Or, for the least try and find the named entities from the conversation in an attempt to make sense of this.

Understanding NLP vs NLU vs NLG

The tokens are then analyzed for their grammatical structure, including the word’s role and different possible ambiguities in meaning. To conclude, distinguishing between NLP and NLU is vital for designing effective language processing and understanding systems. By embracing the differences and pushing the boundaries of language understanding, we can shape a future where machines truly comprehend and communicate with humans in an authentic and effective way.

This transparency makes symbolic AI an appealing choice for those who want the flexibility to change the rules in their NLP model. This is especially important for model longevity and reusability so that you can adapt your model as data is added or other conditions change. And also the intents and entity change based on the previous chats check out below. It is quite common to confuse specific terms in this fast-moving field of Machine Learning and Artificial Intelligence. The above is the same case where the three words are interchanged as pleased. If the user utterances just bounce off the the chatbot and the user needs to figure out how to approach the conversation, without any guidance, the conversation is bound to be abandoned.

Word Embeddings Bert

Bharat Saxena has over 15 years of experience in software product development, and has worked in various stages, from coding to managing a product. With BMC, he supports the AMI Ops Monitoring for Db2 product development team. Bharat holds Masters in Data Science and Engineering from BITS, Pilani. His current active areas of research are conversational AI and algorithmic bias can process text from grammar, structure, typo, and point of view—but it will be NLU that will help the machine infer the intent behind the language text.

Understand the relationship between two entities within your content and identify the type of relation. Analyze the sentiment (positive, negative, or neutral) towards specific target phrases and of the document as a whole. Train Watson to understand the language of your business and extract customized insights with Watson Knowledge Studio. 6 min read – Explore why human resource departments should be at the center of your organization’s strategy for generative AI adoption.

Conversational AI Events

NLU often involves incorporating external knowledge sources, such as ontologies, knowledge graphs, or commonsense databases, to enhance understanding. The technology also utilizes semantic role labeling (SRL) to identify the roles and relationships of words or phrases in a sentence with respect to a specific predicate. Natural language processing is a subset of AI, and it involves programming computers to process massive volumes of language data.

• For machines, human language, also referred to as natural language, is how humans communicate—most often in the form of text.
• The tokens are run through a dictionary that can identify a word and its part of speech.
• Artificial intelligence is critical to a machine’s ability to learn and process natural language.
• It comprises the majority of enterprise data and includes everything from text contained in email, to PDFs and other document types, chatbot dialog, social media, etc.
• You can configure the environment to be conservative and select only keywords from the text.

While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones. Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities. Instead, machines must know the definitions of words and sentence structure, along with syntax, sentiment and intent. Natural language understanding (NLU) is concerned with the meaning of words. It’s a subset of NLP and It works within it to assign structure, rules and logic to language so machines can “understand” what is being conveyed in the words, phrases and sentences in text. When a user makes a request that triggers the #buy_something intent, the assistant’s response should reflect an understanding of what the something is that the customer wants to buy.

A quick overview of the integration of IBM Watson NLU and accelerators on Intel Xeon-based infrastructure with links to various resources. Please visit our pricing calculator here, which gives an estimate of your costs based on the number of custom models and NLU items per month. Detect people, places, events, and other types of entities mentioned in your content using our out-of-the-box capabilities. Surface real-time actionable insights to provides your employees with the tools they need to pull meta-data and patterns from massive troves of data. 2 min read – By acquiring Apptio Inc., IBM has empowered clients to unlock additional value through the seamless integration of Apptio and IBM.

Read more about https://www.metadialog.com/ here.