In natural language processing (NLP), which of the following can introduce ambiguity in interpretation and thereby complicate parsing or understanding?

Introduction / Context:
Ambiguity is a central challenge in natural language processing (NLP). Systems must disambiguate syntax, semantics, and reference to correctly interpret sentences. The question asks which sources can cause ambiguity and thereby increase the difficulty of accurate parsing, tagging, and semantic analysis.

Given Data / Assumptions:

• Syntactic structures can have multiple parses.
• Words often have multiple senses depending on context.
• Pronouns and definite descriptions may lack clear antecedents.

Concept / Approach:

NLP pipelines typically include part-of-speech tagging, parsing, word sense disambiguation, and coreference resolution to address these issues. Each listed factor—syntax, lexical semantics, and reference—can independently or jointly create ambiguity that must be resolved for correct understanding by downstream tasks such as question answering or information extraction.

Step-by-Step Solution:

Verification / Alternative check:

Standard NLP textbooks detail modules dedicated to resolving each kind of ambiguity, confirming their roles in complexity and error rates.

Why Other Options Are Wrong:

Picking only one factor ignores the multi-layered nature of language ambiguity.

None of the above: Incorrect because each factor is a well-known source of ambiguity.

Common Pitfalls:

Assuming syntax alone determines meaning; overlooking context required for sense and reference resolution.

Final Answer:

All of the above