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Quantifying query ambiguity
Open accessAbstract
We develop a measure of a query with respect to a collection of documents with the aim of quantifying the query's ambiguity with respect to those documents. This measure, the clarity score, is the relative entropy between a query language model and the corresponding collection language model. We substantiate that the clarity score measures the coherence and specificity of the language used in documents likely to satisfy the query. We also argue that it provides a suitable quantification of the (lack of) ambiguity of a query with respect to a collection of documents and has potential applications throughout the field of information retrieval. In particular, the clarity score is shown to correlate positively with average precision in evaluations using TREC test collections. Hence, as one example, the clarity score could serve as a predictor of query performance. Systems would then be able to identify vague information requests and respond differently than they would to clear and specific requests.
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“As long studied in the IR community, the formulation of an effective query requires deep understanding of the user's intent, and many of the important context information behind a user intent is not explicitly expressed in the words they wrote [115].”
From Foundations of GenIR (2025) · cites this paper as [115]
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Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842
“As long studied in the IR community, the formulation of an effective query requires deep understanding of the user’s intent, and many of the important context information behind a user intent is not explicitly expressed in the words they wrote [115].”
Metadata
- Authors
- Cronen-Townsend, S., & Croft, W. B.
- Affiliations
- University of Massachusetts Amherst (US)
- ORCID
- Croft, W. Bruce
- Venue
- Proceedings of the second international conference on Human Language Technology Research -
- Published
- 2002
- Pages
- 104
- OpenAlex
- W2030524533
- Cited by
- 132
- References
- 0
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 4 · 2016: 6 · 2015: 12 · 2014: 2 · 2013: 14 · 2012: 11
- FWCI
- 4.59
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Human Language Technology Research (publishedVersion)
- International Conference on Human Language Technology Research
- Funders
- National Science Foundation (9907018)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Keywords
- query ambiguity, Kullback-Leibler divergence, query performance prediction, average precision
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