Question answering systems handle ambiguities in language through various strategies. One common approach is to rely on contextual clues and language models to disambiguate the meaning of the question. These systems use natural language processing techniques to analyze the structure, syntax, and semantics of the question.

  One strategy is to look for specific keywords or phrases in the question that can provide hints about the intended meaning. For example, if the question contains the phrase "capital of France," the system can infer that the question is asking for the capital city of France, rather than other possible interpretations.

  Another approach is to use statistical methods to predict the most probable interpretation of the question based on a large corpus of training data. These systems learn from previous examples to understand the most common ways questions are formulated and the typical answers they expect. By comparing the question to the patterns in the training data, the system can make an educated guess about the intended meaning.

  Question answering systems can also consider the surrounding context to disambiguate the question. They can take into account the previous questions, statements, or conversations that have occurred to better understand the user's intent. By analyzing the context, the system can make more accurate interpretations and provide more relevant answers.

  In some cases, when the system encounters an ambiguity that it cannot resolve with certainty, it can present multiple possible interpretations or ask for clarification from the user. By engaging in interactive dialogue, the system can narrow down the possibilities and eventually provide a satisfactory answer.

  Overall, question answering systems use a combination of techniques such as keyword analysis, statistical modeling, contextual understanding, and interactive dialogue to handle ambiguities in language and provide accurate and relevant answers to user queries.