QueryBuilder: Human-in-the-Loop Query Development for Information Retrieval
Hemanth Kandula, Damianos Karakos, Haoling Qiu, Benjamin Rozonoyer,, Ian Soboroff, Lee Tarlin, Bonan Min

TL;DR
QueryBuilder is an interactive system that enables novice users to efficiently develop fine-grained, cross-lingual IR queries through iterative document relevance marking and neural-based query refinement, significantly reducing effort and language barriers.
Contribution
It introduces a novel human-in-the-loop approach for rapid, fine-grained query development in cross-lingual IR, combining relevance feedback with neural retrieval models.
Findings
Users can create useful queries within 10 minutes per sub-topic.
The system improves retrieval effectiveness in cross-lingual settings.
Novice users can formulate queries in languages they do not understand.
Abstract
Frequently, users of an Information Retrieval (IR) system start with an overarching information need (a.k.a., an analytic task) and proceed to define finer-grained queries covering various important aspects (i.e., sub-topics) of that analytic task. We present a novel, interactive system called , which allows a novice, English-speaking user to create queries with a small amount of effort, through efficient exploration of an English development corpus in order to rapidly develop cross-lingual information retrieval queries corresponding to the user's information needs. QueryBuilder performs near real-time retrieval of documents based on user-entered search terms; the user looks through the retrieved documents and marks sentences as relevant to the information needed. The marked sentences are used by the system as additional information in query formation and…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Data Management and Algorithms · Semantic Web and Ontologies
