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Natural Language Understanding


Natural language understanding (NLU) is a subtopic of the natural language processing (NLP) field of artificial intelligence. NLU focuses primarily on interpreting or understanding the text, and it is typically based on matching the parsed input to an underlying knowledge model or structured ontology.

Why is NLU Important?


NLU encompasses a broad range of applications, from simple understanding tasks, such as interpreting short direct commands, to complex understanding tasks, such as comprehending newspaper articles or maintaining a humanlike conversation.

NLU functionality, even the basic ones, offers enterprises useful ways of directing the actions of a business application, appliance or device based on the natural language inputs received from users and the intent they entail.

Business Impact


There is significant and widespread demand for systems that can understand or interpret natural language, and that can interact with people in a conversational style.

Applications that leverage NLU include chatbots, virtual assistants, text summarization and text content analysis. Other uses include smart vehicles, machinery, and consumer “intelligent” devices and appliances.

In many cases, the effectiveness of NLU will determine the overall satisfaction with the application or appliance.



The fundamental driver for NLU improvements is a more accurate identification of a user’s intent. As a subfield of NLP, which also includes natural language generation (NLG), NLU has a narrower scope and exclusively aims to comprehend what users mean to say.

People have multiple ways of expressing the same thing. Conversely, people may use the same words to convey different meanings.

The NLU functionality will examine the parsed elements of the text and allow an application to interpret what was meant or intended based on its underlying model. NLU plays a significant role in key functions and applications, including:

Chatbots and virtual assistants

NLU enables the chatbot to identify the intent of the user’s input, and, as needed, to extract key entities from that. For example, in the request, “I’d like to order a large mushroom pizza.” Ordering pizza is the intent, and “mushroom” and “large” are entities needed to properly complete the order.

Text categorization and classification

NLU enables systems to analyze and assign text input into predefined categories. Examples include spam filters, document classification and script compliance.

Automatic text summarization

NLU can play a role in creating summaries of longer text sections.

Question answering and semantic parsing

QA systems leverage several of the above-outlined functions to create a natural language interaction.

Content moderation

While user-generated content keeps increasing on social media platforms, solutions to monitor it, detect offensive/inappropriate/harmful content and moderate interactions are increasingly needed. Content that may be subject to moderation can be identified by means of NLU techniques.

Sentiment analysis

NLU helps to identify and measure the sentiment behind an opinion or context.




Complexity: While some NLU objectives are simple, many are complex, which becomes an obstacle to successful usage of NLU. Factors driving complexity include the use of large-scale vocabularies, grammar, ontologies and models. Significant progress is needed on each of these factors before complex applications of NLU are ready for mainstream adoption.

Customization: Many NLU implementations require customization in terms of the factors mentioned above, as well as custom training datasets.

Evolving technology: The optimal techniques for implementing NLU continue to evolve. While methods such as tree graph analysis are well-established, newer methods based on transformer algorithms are just starting to emerge. The tooling and level of complexity required by each application significantly vary for each use case.

Bundling: NLUs are often bundled within a chatbot platform and are managed via an integrated developer environment. As a result, they are often not separately reviewed.

Want +35% Accuracy, With -65% Cost in Your Text Data Analytics?

Shine a light on your hidden text data insights, anywhere in the world, with Blind Vision.

  Blind Vision


We have dubbed our proprietary web text data extraction and labeling technology, Blind Vision. It is now, arguably, the best in class globally and superior to the established and trusted Computer Vision and other technologies.

Test studies* have been conducted versus a US based web data integration platform that uses Computer Vision, and the results are conclusive. They showed a +35% increase in accuracy and -65% lower running costs using Blind Vision technology.

We like to remain a little secretive about the ‘sauce’, but we can say that Blind Vision combines sophisticated Raw Code Processing algorithms and our own deep learning network architecture.


  Advanced Sentiment Analysis


Sentiment analysis is a very powerful tool with many commercial applications. However, it is very difficult to do well. Many claim to have a sentiment solution, but upon analysis, few in the market really do. Current solutions suffer from poor consistency, limited accuracy and lack of advanced deep learning techniques.

Humans can easily judge the polarity of text, unlike machines. We have developed an apex sentiment tool, using our proprietary neural network architecture, which enables real Natural Language Understanding (NLU) and emulates how humans judge the content and context of text.

Our solution provides consistent sentiment analysis of high or low-frequency content, in long or short format, across 100+ languages. And, we can do all of this with an impressive F1 score of 0.9443!



  Named Entity Recognition


Valuable (business) information is buried in a largely text-based (79%) data explosion, most of which also resides in unstructured data on the web. The ability to extract, organize, analyze and connect large amounts of unstructured text data has become of paramount importance.

Extracting, classifying, and connecting entities via Named Entity Recognition (NER) technology plays an important role in sorting unstructured data and identifying valuable information. NER is a key foundational block for any information discovery pipeline and the basis for most Natural Language Processing (NLP) solutions.

We have built the new industry standard: Multilingual NER (in 100+ languages) that is unrivaled in accuracy vs. current ‘open source’ solutions and performs with an F1 score of 0.95.



  Web Scraping


Web Scraping may sound easy, but it’s not! We have solved the 5 most prevalent issues faced by standard web scraping methods.

One key issue involves constant website layout changes. SEO improvements and UX/UI changes are delivered through HTML layout amendments. As a result, element locators that web scrapers are configured to in order to extract data, change and break the scraping process by extracting incorrect or no data. It takes a lot of manual effort to update these configurations and maintaining thousands of sources becomes near impossible.

We have fully automated the process of source reconfiguration to present you with a truly scalable, leading-edge web scraping solution. One that operates in 100+ languages and can scrape any text data from any web source in real-time.


  Automated Text Classification


We have built an industry leading, multilingual, automated text classification capability that demonstrates superior accuracy (underpinned by Natural Language Understanding), uses no language translation layer (which significantly distorts the meaning of content) and is able to proces all length content, via our single, one stop shop, platform. 

These accuracy levels now mimic human understanding of any text.


  BERT Semantic Similarities


Research into the possibilities of using the multilingual BERT model for determining semantic similarities of news content.


  Semantic Similarity of
Arbitrary-length Text Content

Research on the specific features of determining the semantic similarity of arbitrary–length text content using multilingual Transformer based models.


Read our latest Blog - Native vs. Translation Learn More >>