NLU (Natural Language Understanding)
It is a subfield of NLP that focuses on interpreting the meaning of natural language to better understand its context using syntactic and semantic analysis. Some of the most common tasks included in NLU are:
- Semantic analysis
- Intent recognition
- Entity recognition
- Sentiment analysis
The syntactic analysis used by NLU in its operations corrects sentence structure and extracts exact or dictionary meanings from the text. On the other hand, semantic analysis analyzes the grammatical format of sentences, including the arrangement of sentences, words and clauses.
Humans have the natural ability to understand a sentence and its context. However, with machines, understanding the true meaning of the input provided is not easy to understand.
Therefore, the software exploits these arrangements in semantic analysis to define and determine relationships between independent words and phrases in a specific context. The software learns and develops meanings through these combinations of sentences and words and provides better results to users.
NLU Applications
Here are some applications of NLU:
- Automated customer service systems.
- Intelligent virtual assistants
- Search engine
- Enterprise Chatbots
NLG (Natural Language Generation)
This is a subfield of NLP that focuses more on generating natural language from structured data. Unlike NLP and NLU, the main goal of NLG is to create human language responses and convert the data into a speech format.
NLG uses a three-phase system to ensure its success and provide accurate results. Its linguistic rules are based on morphology, lexicons, syntax and semantics. The three phases he uses in his approach are:
Applications of NLG
Here are some of the applications of NLG:
- Business Analytics Intelligence
- Financial forecast
- Customer Service Chatbots
- Summary generation
What is the difference between NLP, NLU and NLG?
As mentioned at the beginning of the blog, NLP is a branch of AI, while NLU and NLG are subsets of NLP. Natural language processing aims to understand the user's command and generate an appropriate response.
NLU, on the one hand, can interact with the computer using natural language. NLU is programmed to decipher the intent of the command and provide accurate outputs even if the input consists of pronunciation errors in the sentence.
NLG, on the other hand, is above NLU, which can offer users smoother, more engaging, and more exciting responses than a normal human would give. NLG identifies the essence of the document and, based on these analyses, generates very precise answers.
Conclusion
In summary, NLP converts unstructured data into a structured format so that the software can understand the given inputs and respond appropriately. Conversely, NLU aims to understand the meaning of sentences, while NLG focuses on formulating correct sentences with the right intention in specific languages based on the dataset. Refer to our Shaip experts to learn more about these technologies.
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