Ambiguous intent refers to keywords where the searcher is unclear and also requires additional information. If a query is vague, the internet cannot accurately determine the searcher's intent.

A typical method for processing search queries is to obtain a set of matching documents and then rank them using an importance scoring function. This straightforward method generally works well for specific and particular search queries. However, sometimes this technique fails.

When a search query is broad (e.g., “T-shirts”), it’s not clear exactly how to decide which matching results are among the most relevant. Even worse, when the query is ambiguous (e.g., “mixers”), it’s not only unclear how to determine matching results, let alone how to place them.

Can a search engine instantly detect when a search query has broad or ambiguous intent? No single method is best, but here are some helpful signs: Certain searches tend to have tiny result sets.

On the other hand, broad and uncertain queries tend to have large sets of results. If this number is high, the question is debatable or possibly broad.

Difference in search results when using ambiguous intent.

A stronger indicator than the size of the result set is its variance. This difference can be verified from the similarity of the paired results (e.g., cosine interval using a word-embedding version) or from a histogram summarizing the collection of results (e.g., worsening of group circulation). A large difference indicates an ambiguous or broad investigation.

The distinctness of the results. Another indicator is the diversity of the results in relation to those of the total collection of records, typically measured using the Kullback-Leibler divergence .

For a more in-depth study of this, as well as strategies , I suggest Claudia Hauff's argument on "Anticipating the performance of queries and access systems."

Query analysis and ambiguous intent

Brief searches tend to be broad and are also more likely to be uncertain. Handling the query with a grammar class or entity recognition tagger can produce a much more accurate assessment. Hauff discusses these types of methods in his section on pre-retrieval predictors.

An even more modern approach would take advantage of word embeddings

In contrast, broad and unclear queries have lower click-through rates and fewer clicks on ranking . Broad and ambiguous searches also have higher pagination, question enhancement, and question rephrasing costs.

It is possible to use labeled queries to educate a version of understanding that identifies vague and broad queries – although any approach based on the researcher's historical habits is prone to presentation bias.