a. Lexical ambiguity
It’s predefined at a very primitive level such as word-level.

b. Syntax Level ambiguity
Basically, in this, we can define a sentence in a parsed way in different ways.

c. Referential ambiguity
Generally, referential ambiguity says that we have to refer something using pronouns only.

When discussing difficulties in Natural Language Understanding (NLU) in an artificial intelligence interview, it’s essential to demonstrate a comprehensive understanding of the challenges involved. Here’s a structured answer:

Ambiguity: Natural language is inherently ambiguous. Words and phrases can have multiple meanings depending on the context. Resolving this ambiguity requires sophisticated algorithms and contextual understanding.
Contextual Understanding: Understanding the context in which a statement is made is crucial for accurate interpretation. Context can include previous dialogue, cultural references, and situational factors. Building systems that can effectively capture and utilize context is a significant challenge.
Variability: Language is highly variable, with diverse dialects, slang, and colloquialisms. NLU systems must be robust enough to understand and interpret language variations across different regions and demographics.
Lack of Standardization: Unlike formal languages like programming languages, natural language lacks strict rules and standards. This lack of standardization makes it challenging to develop NLU systems that consistently interpret language input correctly.
Data Sparsity: Training NLU models requires vast amounts of annotated data. However, obtaining labeled data for every possible linguistic nuance or scenario is impractical. As a result, NLU systems may struggle with understanding less common or specialized language patterns.
Commonsense Reasoning: Understanding natural language often requires reasoning about common sense knowledge and world facts. Integrating this knowledge into NLU systems remains a significant challenge, as it involves capturing and representing a vast array of domain-specific and general knowledge.
Inference and Reasoning: NLU often requires the ability to perform complex inference and reasoning tasks. This includes tasks such as understanding implicit meaning, making logical deductions, and resolving references. Developing algorithms that can perform such tasks accurately and efficiently is non-trivial.
Multimodal Understanding: With the increasing prevalence of multimodal data (combining text, images, audio, etc.), NLU systems must be capable of understanding and integrating information from multiple modalities. Achieving effective multimodal understanding presents additional technical challenges.
Ethical and Societal Implications: NLU systems can have significant societal impacts, including biases, privacy concerns, and misinformation propagation. Addressing these ethical and societal implications requires careful consideration and proactive measures during system design and deployment.

By addressing these challenges, researchers and engineers can advance the state-of-the-art in NLU and develop more robust and reliable AI systems for understanding and interacting with human language.