Ambiguous queries occur when a user’s search terms have multiple possible meanings or interpretations. IR systems handle ambiguity through various strategies, such as disambiguating queries using context, synonyms, and user intent.
Natural language processing (NLP) techniques like query expansion (adding related terms to clarify the user's intent) and word sense disambiguation (selecting the most likely meaning of a word based on context) are commonly used. For example, if a user searches for "apple," the system might check for context to determine whether the user is referring to the fruit or the technology company.
Machine learning models, such as classification algorithms that assess user preferences, can also help resolve ambiguity by learning from previous user interactions and refining future query interpretations.