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Recognizing Medical Search Query Intent by Few-shot Learning

Published: 07 July 2022 Publication History

Abstract

Online healthcare services can provide unlimited and in-time medical information to users, which promotes social goods and breaks the barriers of locations. However, understanding the user intents behind the medical related queries is a challenging problem. Medical search queries are usually short and noisy, lack strict syntactic structure, and also require professional background to understand the medical terms. The medical intents are fine-grained, making them hard to recognize. In addition, many intents only have a few labeled data. To handle these problems, we propose a few-shot learning method for medical search query intent recognition called MEDIC. We extract co-click queries from user search logs as weak supervision to compensate for the lack of labeled data. We also design a new query encoder which learns to represent queries as a combination of semantic knowledge recorded in an external medical knowledge graph, syntactic knowledge which marks the grammatical role of each word in the query, and generic knowledge which is captured by language models pretrained from large-scale text corpus. Experimental results on a real medical search query intent recognition dataset validate the effectiveness of MEDIC.

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Presentation video for MEDIC.

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2022

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Author Tags

  1. co-click query analysis
  2. few-shot learning
  3. graph representation learning
  4. knowledge graph
  5. medical search query understanding
  6. query intent recognition

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  • (2024)Reinforced Self-Supervised Training for Few-Shot LearningIEEE Signal Processing Letters10.1109/LSP.2024.337048831(731-735)Online publication date: 2024
  • (2024)Optimizing Question Answering Systems in Education: Addressing Domain-Specific ChallengesIEEE Access10.1109/ACCESS.2024.348322412(156572-156587)Online publication date: 2024
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