NLP vs NLU: from Understanding a Language to Its Processing by Sciforce Sciforce

nlp nlu

Intent recognition and sentiment analysis are the main outcomes of the NLU. Thus, it helps businesses to understand customer needs and offer them personalized products. Natural Language Understanding Applications are becoming increasingly important in the business world. NLUs require specialized skills in the fields of AI and machine learning and this can prevent development teams that lack the time and resources to add NLP capabilities to their applications.

nlp nlu

A task called word sense disambiguation, which sits under the NLU umbrella, makes sure that the machine is able to understand the two different senses that the word “bank” is used. Natural languages are different from formal or constructed languages, which have a different origin and development path. For example, programming languages including C, Java, Python, and many more were created for a specific reason. NLG also encompasses text summarization capabilities that generate summaries from in-put documents while maintaining the integrity of the information. Extractive summarization is the AI innovation powering Key Point Analysis used in That’s Debatable.

Top 10 AI Tools for NLP: Enhancing Text Analysis

NLP is a branch of AI that allows more natural human-to-computer communication by linking human and machine language. Each plays a unique role at various stages of a conversation between a human and a machine. Let’s illustrate this example by using a famous NLP model called Google Translate. As seen in Figure 3, Google translates the Turkish proverb “Damlaya damlaya göl olur.” as “Drop by drop, it becomes a lake.” This is an exact word by word translation of the sentence. For example, using NLG, a computer can automatically generate a news article based on a set of data gathered about a specific event or produce a sales letter about a particular product based on a series of product attributes. Simply put, using previously gathered and analyzed information, computer programs are able to generate conclusions.

The terms might look like alphabet spaghetti but each is a separate concept. In fact, NLP includes NLU and NLG concepts to achieve human-like processing. DST is essential at this stage of the dialogue system and is responsible for multi-turn conversations.

NLP vs NLU vs. NLG summary

Developed by Google, BERT is a pre-trained transformer model designed for bidirectional representation of text. BERT excels in understanding context and semantics, making it highly effective for tasks such as sentiment analysis, question answering, and named entity recognition. While NLP converts the raw data into structured data for its processing, NLU enables the computers to understand the actual intent of structured data. NLP is capable of processing simple sentences,NLP cannot process the real intent or the actual meaning of complex sentences. NLP takes input text in the form of natural language, converts it into a computer language, processes it, and returns the information as a response in a natural language.

nlp nlu

For example, in medicine, machines can infer a diagnosis based on previous diagnoses using IF-THEN deduction rules. Using complex algorithms that rely on linguistic rules and AI machine training, Google Translate, Microsoft Translator, and Facebook Translation have become leaders in the field of “generic” language translation. NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans. Parse sentences into subject-action-object form and identify entities and keywords that are subjects or objects of an action. Analytics Insight® is an influential platform dedicated to insights, trends, and opinion from the world of data-driven technologies.

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. Together with NLG, they will be able to easily help in dealing and interacting with human customers and carry out various other natural language-related operations in companies and businesses. For example, NLP allows speech recognition to capture spoken language in real-time, transcribe it, and return text- NLU goes an extra step to determine a user’s intent. NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways.

  • NLTK, a comprehensive library for NLP, has been a staple in the field for years.
  • NLU leverages AI algorithms to recognize attributes of language such as sentiment, semantics, context, and intent.
  • People can say identical things in numerous ways, and they may make mistakes when writing or speaking.

It enables computers to evaluate and organize unstructured text or speech input in a meaningful way that is equivalent to both spoken and written human language. Natural language understanding is a branch of AI that understands sentences using text or speech. NLU allows machines to understand human interaction by using algorithms to reduce human speech into structured definitions and concepts for understanding relationships. 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. Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability.


For those interested, here is our benchmarking on the top sentiment analysis tools in the market. Semantic analysis, the core of NLU, involves applying computer algorithms to understand the meaning and interpretation of words and is not yet fully resolved. By default, virtual assistants tell you the weather for your current location, unless you specify a particular city. The goal of question answering is to give the user response in their natural language, rather than a list of text answers. NLP is growing increasingly sophisticated, yet much work remains to be done.

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The verb that precedes it, swimming, provides additional context to the reader, allowing us to conclude that we are referring to the flow of water in the ocean. The noun it describes, version, denotes multiple iterations of a report, enabling us to determine that we are referring to the most up-to-date status of a file. While Natural Language Processing is concerned with the linguistic nlp nlu aspect of a language Natural Language Understanding is concerned about its intent. Though different to an extent their correlation is what is driving the change in various modern day industries. NLP and NLU are so closely related that at times these terms are used interchangeably. Transcreation ensures that every line in the sentence is not converted directly into the desired language.

NLP vs. NLU: What’s the Difference and Why Does it Matter?

Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment. Since customers’ input is not standardized, chatbots need powerful NLU capabilities to understand customers. When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing. As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills.

  • NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, understanding the input, or generating a response.
  • On our quest to make more robust autonomous machines, it is imperative that we are able to not only process the input in the form of natural language, but also understand the meaning and context—that’s the value of NLU.
  • He led technology strategy and procurement of a telco while reporting to the CEO.
  • With text analysis solutions like MonkeyLearn, machines can understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket.
  • If the evaluator is not able to reliably tell the difference between the response generated by the machine and the other human, then the machine passes the test and is considered to be exhibiting “intelligent” behavior.