Enhancing Healthcare Search Intent Recognition with Query Representation Learning and Session Context

• URL: http://arxiv.org/abs/2605.10021v1
• Date: Mon, 11 May 2026 05:44:54 GMT
• Title: Enhancing Healthcare Search Intent Recognition with Query Representation Learning and Session Context
• Abstract summary: We introduce a novel loss function designed to capture the multifaceted nature of health search queries.<n>We quantify the ambiguity of health queries and the misalignment between global search intents and those discerned from individual sessions.
• Score: 7.918995314783813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classifying the intent behind healthcare search queries is crucial for improving the delivery of online healthcare information. The intricate nature of medical search queries, coupled with the limited availability of high-quality labeled data, presents substantial challenges for developing efficient classification models. Previous studies have exploited user interaction data, such as user clicks from search logs and employed pairwise loss functions to model co-click behavior for query representation learning. However, many health queries could have multiple intents, resulting in ambiguous or divergent click behavior. Furthermore, learning the single most popular intent of queries as inferred from global statistics based on the aggregate behavior of different users could potentially lead to disparity and performance drop when classifying the query intent within specific search sessions. To address these limitations, our work improves the query representation learning by aggregating similar queries via clustering, and introducing a novel loss function designed to capture the multifaceted nature of health search queries, resulting in a more scalable and accurate learning procedure. Furthermore, we quantify the ambiguity of health queries and the misalignment between global search intents and those discerned from individual sessions, by introducing the concordance rate (CR) score, and demonstrate a simple and effective method for incorporating our learned query representation into contextual, session-based search intent classification. Our extensive experimental results and analysis on two real-world search log datasets, i.e., a Health Search (HS) dataset and the publicly available TripClick dataset, demonstrate that our approach not only improves the intrinsic clustering metrics for query representation learning but also enhances accuracy for subsequent search intent classification tasks.

Related papers

• Rethinking Deep Research from the Perspective of Web Content Distribution Matching [86.90255285277256]
  We propose WeDas, a Web Content Distribution Aware framework that incorporates search-space structural characteristics into the agent's observation space.<n>We introduce a few-shot probing mechanism that iteratively estimates the Query-Result Alignment Score.<n>As a plug-and-play module, WeDas consistently improves sub-goal completion and accuracy across four benchmarks.
  arXiv  Detail & Related papers  (2026-03-07T14:51:17Z)
• Deep Learning-Based Approach for Improving Relational Aggregated Search [0.46664938579243564]
  This research investigates the application of advanced natural language processing techniques, namely stacked autoencoders and AraBERT embeddings.<n>By transcending the limitations of traditional search engines, we offer more enriched, context-aware characterizations of search results.
  arXiv  Detail & Related papers  (2025-10-01T14:37:38Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval [15.757140563856675]
  This work introduces a novel task that focuses on suggesting minimal textual modifications needed to explore visually consistent subsets of the collection.<n>To facilitate the evaluation and development of methods, we present a tailored benchmark named CroQS.<n> Baseline methods from related fields, such as image captioning and content summarization, are adapted for this task to provide reference performance scores.
  arXiv  Detail & Related papers  (2024-12-18T13:24:09Z)
• Query-oriented Data Augmentation for Session Search [71.84678750612754]
  We propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
  arXiv  Detail & Related papers  (2024-07-04T08:08:33Z)
• Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning [25.356999988217325]
  Relevance modeling aims to locate desirable items for corresponding queries. auxiliary query-item interactions extracted from user historical behavior data could provide hints to reveal users' search intents further. Our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views.
  arXiv  Detail & Related papers  (2023-08-10T06:52:53Z)
• Semantic Equivalence of e-Commerce Queries [6.232692545488813]
  This paper introduces a framework to recognize and leverage query equivalence to enhance searcher and business outcomes. The proposed approach addresses three key problems: mapping queries to vector representations of search intent, identifying nearest neighbor queries expressing equivalent or similar intent, and optimizing for user or business objectives.
  arXiv  Detail & Related papers  (2023-08-07T18:40:13Z)
• Graph Enhanced BERT for Query Understanding [55.90334539898102]
  query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. In recent years, pre-trained language models (PLMs) have advanced various natural language processing tasks. We propose a novel graph-enhanced pre-training framework, GE-BERT, which can leverage both query content and the query graph.
  arXiv  Detail & Related papers  (2022-04-03T16:50:30Z)
• Exposing Query Identification for Search Transparency [69.06545074617685]
  We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems. We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
  arXiv  Detail & Related papers  (2021-10-14T20:19:27Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.